InfoVis:Wiki - User contributions [en]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-12-20T18:02:10Z

UE-InfoVis0506 0306110: /* Visual Mapping */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.
# The action happens at a point of time, therefore we have practically no data flow (bec. the number of simultaneous actions is zero or one).
# The data needs aggregation and is only representabel through actions in a ''time intervall''.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Stacked Barplot:''' The Actions in an intervall (e.g. 1 hour) are counted and represented as stacked bars. Moreover the different parts of the bars are connected with lines to give an idea what is the development from one intervall to the next.

# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Stacked Barplot====
# Bars represent actions in a time intervall (e.g. one hour).
# Space between bars leave room for printing lines to give an ide of the trend.
# Difference in colour represents the specific action (e.g. green = VIEW, red = BUY, etc.)
# Actions are stacked over each other, but with fixed order (VIEW before BUY before RATE etc.)



==== Mosaicplot ====
Represent countdata from different factors with more levels as plots where the areas represent the values of the cells of the underlying contingency table. This gives a graphical test of independency of the actual values from the combinations of the factors.

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# When selecting more than 3 variables a subset out of htese variables is used to construct the scatterplot. An iteration takes place over time and exchanges variables continously to show all the selected variables.
# To make the graphic more readable the user can add color with respect to certain variables.

====Morphing / Dynamic Queries====
* Specify start and end point.
* Hit play to let morphing happen within the assigned timespan.
* Hit STOP to set the header to the starting time.
* Hit PAUSE to stop the header at any time.
* Drag and Drop the head to jump to a specific time.
* Switch between an absolute and relative time-axis.
* The start and end point are also giving the span for the dynamic queries applied on the active (selected) methods.

====Time====
* The time is represented ''interactivelly'' by the timeline in the upper part.
* Additionally the current time and date are represented by an optional calender / clock object.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (dynamic queries).
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).
# When more than one visualizations are selected, one can klick and hold to zoom the spezified technique larger (Zooming). The others move to the background but stay visible.

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

[[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3 - Prototype|Prototype]]

====Event Log====
* 28.11.2005 - Visualisation techniques are now selectable NEXT to one another (switches, no tables).
* 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
* 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
* 22.11.2005 - placement of first version of MICE tool
* 20.12.2005 - final prototype released
* 20.12.2005 - short description online

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3 - Prototype

2005-12-20T18:00:51Z

UE-InfoVis0506 0306110: /* Links */

===Short Description MICE Tool (Version 4)===

====Titel====
MICE (Music Clustering and Investigation Environment)

====Authors====
Stefan Schnabl (0306110)
Heinrich Fritz (0325693)
Leonhard Seyfang (0227022)

====Short Introduction to the Task====
The Task was to enable web developers and people running an online music portal (including a store) to create hypothesis about the ongoing development strategy via a visualisation tool.
Basically one should be able to investigate the data and find out certain hypothesis about what he or she thinks is going on. This includes things like

# user habits
# dependencies between certain attributes
# dependencies to the time
# etc.

====Short Introduction to the Realization====
The prototype is done in Flash MX 2004 pro. It makes heavy use of Action Script (2.0). The data is driven from an XML file ("source.xml") which has to be stored with (in the same directory as) the .swf file of the Tool.
The Tool provies 4 Methods for visualization:

# Barchart
# Mosaicplot
# Scatterplot
# Date and Time

These methods can be put together next to each othter. Thex move parallel when the playhead is started. All methods are dependent on the TIMELINE, which represents the data in dependency to ist occurence time. One can focus on special time spans by moving the outer bounds of the timebar. Odinary PLAY, STOP and PAUSE button help to use the timebar very intuetively.
The right side of the application provides the part where the user can select the attributes the shown data will represent as well as teh granularity. The granularity means the time span in which the data is counted. By that one can sort of zoom into the data when selecting smaller granularity.

====Screenshot====
[[Image:Screenshot_2.jpg]] (MICE Tool with active Barchart - method)

====Links====
Presentation 
http://www.sunarts.at/Studium/InfoVis/pres/MICEpres.pdf 
Konzept 
[[Teaching:TUW_-_UE_InfoVis_WS_2005/06_-_Gruppe_G8_-_Aufgabe_3]] 
InfoVis Wiki Home 
http://www.infovis-wiki.net/index.php/Main_Page

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]] 
<<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06_-_Gruppe_G8_-_Aufgabe_3|Aufgabe 3]]

File:Screenshot 2.jpg

2005-12-20T18:00:36Z

UE-InfoVis0506 0306110: Screenshot of MICE Tool.

== Summary ==
Screenshot of MICE Tool.
== Copyright status ==
[Schnabl, Seyfang, Fritz, 2005]
== Source ==

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3 - Prototype

2005-12-20T18:00:10Z

UE-InfoVis0506 0306110:

===Short Description MICE Tool (Version 4)===

====Titel====
MICE (Music Clustering and Investigation Environment)

====Authors====
Stefan Schnabl (0306110)
Heinrich Fritz (0325693)
Leonhard Seyfang (0227022)

====Short Introduction to the Task====
The Task was to enable web developers and people running an online music portal (including a store) to create hypothesis about the ongoing development strategy via a visualisation tool.
Basically one should be able to investigate the data and find out certain hypothesis about what he or she thinks is going on. This includes things like

# user habits
# dependencies between certain attributes
# dependencies to the time
# etc.

====Short Introduction to the Realization====
The prototype is done in Flash MX 2004 pro. It makes heavy use of Action Script (2.0). The data is driven from an XML file ("source.xml") which has to be stored with (in the same directory as) the .swf file of the Tool.
The Tool provies 4 Methods for visualization:

# Barchart
# Mosaicplot
# Scatterplot
# Date and Time

These methods can be put together next to each othter. Thex move parallel when the playhead is started. All methods are dependent on the TIMELINE, which represents the data in dependency to ist occurence time. One can focus on special time spans by moving the outer bounds of the timebar. Odinary PLAY, STOP and PAUSE button help to use the timebar very intuetively.
The right side of the application provides the part where the user can select the attributes the shown data will represent as well as teh granularity. The granularity means the time span in which the data is counted. By that one can sort of zoom into the data when selecting smaller granularity.

====Screenshot====
[[Image:Screenshot_2.jpg]] (MICE Tool with active Barchart - method)

====Links====
Presentation 
http://www.sunarts.at/Studium/InfoVis/pres/MICEpres.pdf 
Konzept 
[[Teaching:TUW_-_UE_InfoVis_WS_2005/06_-_Gruppe_G8_-_Aufgabe_3]] 
InfoVis Wiki Home 
http://www.infovis-wiki.net/index.php/Main_Page

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]
<<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06_-_Gruppe_G8_-_Aufgabe_3|Aufgabe 3]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3 - Prototype

2005-12-20T17:59:58Z

UE-InfoVis0506 0306110:

===Short Description MICE Tool (Version 4)===

====Titel====
MICE (Music Clustering and Investigation Environment)

====Authors====
Stefan Schnabl (0306110)
Heinrich Fritz (0325693)
Leonhard Seyfang (0227022)

====Short Introduction to the Task====
The Task was to enable web developers and people running an online music portal (including a store) to create hypothesis about the ongoing development strategy via a visualisation tool.
Basically one should be able to investigate the data and find out certain hypothesis about what he or she thinks is going on. This includes things like

# user habits
# dependencies between certain attributes
# dependencies to the time
# etc.

====Short Introduction to the Realization====
The prototype is done in Flash MX 2004 pro. It makes heavy use of Action Script (2.0). The data is driven from an XML file ("source.xml") which has to be stored with (in the same directory as) the .swf file of the Tool.
The Tool provies 4 Methods for visualization:

# Barchart
# Mosaicplot
# Scatterplot
# Date and Time

These methods can be put together next to each othter. Thex move parallel when the playhead is started. All methods are dependent on the TIMELINE, which represents the data in dependency to ist occurence time. One can focus on special time spans by moving the outer bounds of the timebar. Odinary PLAY, STOP and PAUSE button help to use the timebar very intuetively.
The right side of the application provides the part where the user can select the attributes the shown data will represent as well as teh granularity. The granularity means the time span in which the data is counted. By that one can sort of zoom into the data when selecting smaller granularity.

====Screenshot====
[[Image:Screenshot_2.jpg]] (MICE Tool with active Barchart - method)

====Links====
Presentation 
http://www.sunarts.at/Studium/InfoVis/pres/MICEpres.pdf 
Konzept 
[[Teaching:TUW_-_UE_InfoVis_WS_2005/06_-_Gruppe_G8_-_Aufgabe_3 
InfoVis Wiki Home 
http://www.infovis-wiki.net/index.php/Main_Page

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]
<<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06_-_Gruppe_G8_-_Aufgabe_3|Aufgabe 3]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3 - Prototype

2005-12-20T17:56:41Z

UE-InfoVis0506 0306110:

===Short Description MICE Tool (Version 4)===

====Titel====
MICE (Music Clustering and Investigation Environment)

====Authors====
Stefan Schnabl (0306110)
Heinrich Fritz (0325693)
Leonhard Seyfang (0227022)

====Short Introduction to the Task====
The Task was to enable web developers and people running an online music portal (including a store) to create hypothesis about the ongoing development strategy via a visualisation tool.
Basically one should be able to investigate the data and find out certain hypothesis about what he or she thinks is going on. This includes things like

# user habits
# dependencies between certain attributes
# dependencies to the time
# etc.

====Short Introduction to the Realization====
The prototype is done in Flash MX 2004 pro. It makes heavy use of Action Script (2.0). The data is driven from an XML file ("source.xml") which has to be stored with (in the same directory as) the .swf file of the Tool.
The Tool provies 4 Methods for visualization:

# Barchart
# Mosaicplot
# Scatterplot
# Date and Time

These methods can be put together next to each othter. Thex move parallel when the playhead is started. All methods are dependent on the TIMELINE, which represents the data in dependency to ist occurence time. One can focus on special time spans by moving the outer bounds of the timebar. Odinary PLAY, STOP and PAUSE button help to use the timebar very intuetively.
The right side of the application provides the part where the user can select the attributes the shown data will represent as well as teh granularity. The granularity means the time span in which the data is counted. By that one can sort of zoom into the data when selecting smaller granularity.

====Screenshot====
[[Image:Screenshot_2.jpg]] (MICE Tool with active Barchart - method)

====Links====
[[http://www.sunarts.at/Studium/InfoVis/pres/MICEpres.pdf|Presentation]] 
[[http://www.infovis-wiki.net/index.php/Teaching:TUW_-_UE_InfoVis_WS_2005/06_-_Gruppe_G8_-_Aufgabe_3|Koncept of the application]] 
[[http://www.infovis-wiki.net/index.php/Main_Page|InfoVis WIKI]] 

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3 - Prototype

2005-12-20T17:55:49Z

UE-InfoVis0506 0306110:

===Short Description MICE Tool (Version 4)===

====Titel====
MICE (Music Clustering and Investigation Environment)

====Authors====
Stefan Schnabl (0306110)
Heinrich Fritz (0325693)
Leonhard Seyfang (0227022)

====Short Introduction to the Task====
The Task was to enable web developers and people running an online music portal (including a store) to create hypothesis about the ongoing development strategy via a visualisation tool.
Basically one should be able to investigate the data and find out certain hypothesis about what he or she thinks is going on. This includes things like

# user habits
# dependencies between certain attributes
# dependencies to the time
# etc.

====Short Introduction to the Realization====
The prototype is done in Flash MX 2004 pro. It makes heavy use of Action Script (2.0). The data is driven from an XML file ("source.xml") which has to be stored with (in the same directory as) the .swf file of the Tool.
The Tool provies 4 Methods for visualization:

# Barchart
# Mosaicplot
# Scatterplot
# Date and Time

These methods can be put together next to each othter. Thex move parallel when the playhead is started. All methods are dependent on the TIMELINE, which represents the data in dependency to ist occurence time. One can focus on special time spans by moving the outer bounds of the timebar. Odinary PLAY, STOP and PAUSE button help to use the timebar very intuetively.
The right side of the application provides the part where the user can select the attributes the shown data will represent as well as teh granularity. The granularity means the time span in which the data is counted. By that one can sort of zoom into the data when selecting smaller granularity.

====Screenshot====
[[Image:Screenshot_2.jpg]] (MICE Tool with active Barchart - method)

====Links====
[[http://www.sunarts.at/Studium/InfoVis/pres/MICEpres.pdf|Presentation]] 
[[http://www.infovis-wiki.net/index.php/Teaching:TUW_-_UE_InfoVis_WS_2005/06_-_Gruppe_G8_-_Aufgabe_3|Koncept of the application]] 
[[http://www.infovis-wiki.net/index.php/Main_Page|InfoVis WIKI]]

File:Screenshot 2.JPG

2005-12-20T17:55:19Z

UE-InfoVis0506 0306110: Screenshot of MICE Tool with Barchart in the picture.

== Summary ==
Screenshot of MICE Tool with Barchart in the picture.
== Copyright status ==
[Schnabl, Seyfang, Fritz, 2005]
== Source ==

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-12-04T13:03:55Z

UE-InfoVis0506 0306110: /* Special Issues */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.
# The action happens at a point of time, therefore we have practically no data flow (bec. the number of simultaneous actions is zero or one).
# The data needs aggregation and is only representabel through actions in a ''time intervall''.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Stacked Barplot:''' The Actions in an intervall (e.g. 1 hour) are counted and represented as stacked bars. Moreover the different parts of the bars are connected with lines to give an idea what is the development from one intervall to the next.

# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Stacked Barplot====
# Bars represent actions in a time intervall (e.g. one hour).
# Space between bars leave room for printing lines to give an ide of the trend.
# Difference in colour represents the specific action (e.g. green = VIEW, red = BUY, etc.)
# Actions are stacked over each other, but with fixed order (VIEW before BUY before RATE etc.)



====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# When selecting more than 3 variables a subset out of htese variables is used to construct the scatterplot. An iteration takes place over time and exchanges variables continously to show all the selected variables.
# To make the graphic more readable the user can add color with respect to certain variables.

====Morphing / Dynamic Queries====
* Specify start and end point.
* Hit play to let morphing happen within the assigned timespan.
* Hit STOP to set the header to the starting time.
* Hit PAUSE to stop the header at any time.
* Drag and Drop the head to jump to a specific time.
* Switch between an absolute and relative time-axis.
* The start and end point are also giving the span for the dynamic queries applied on the active (selected) methods.

====Time====
* The time is represented ''interactivelly'' by the timeline in the upper part.
* Additionally the current time and date are represented by an optional calender / clock object.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (dynamic queries).
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).
# When more than one visualizations are selected, one can klick and hold to zoom the spezified technique larger (Zooming). The others move to the background but stay visible.

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
* 28.11.2005 - Visualisation techniques are now selectable NEXT to one another (switches, no tables).
* 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
* 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
* 22.11.2005 - placement of first version of MICE tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-12-04T11:56:19Z

UE-InfoVis0506 0306110: /* Visual Mapping */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Stacked Barplot:''' The Actions in an intervall (e.g. 1 hour) are counted and represented as stacked bars. Moreover the different parts of the bars are connected with lines to give an idea what is the development from one intervall to the next.

# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Stacked Barplot====
# Bars represent actions in a time intervall (e.g. one hour).
# Space between bars leave room for printing lines to give an ide of the trend.
# Difference in colour represents the specific action (e.g. green = VIEW, red = BUY, etc.)
# Actions are stacked over each other, but with fixed order (VIEW before BUY before RATE etc.)



====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# When selecting more than 3 variables a subset out of htese variables is used to construct the scatterplot. An iteration takes place over time and exchanges variables continously to show all the selected variables.
# To make the graphic more readable the user can add color with respect to certain variables.

====Morphing / Dynamic Queries====
* Specify start and end point.
* Hit play to let morphing happen within the assigned timespan.
* Hit STOP to set the header to the starting time.
* Hit PAUSE to stop the header at any time.
* Drag and Drop the head to jump to a specific time.
* Switch between an absolute and relative time-axis.
* The start and end point are also giving the span for the dynamic queries applied on the active (selected) methods.

====Time====
* The time is represented ''interactivelly'' by the timeline in the upper part.
* Additionally the current time and date are represented by an optional calender / clock object.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (dynamic queries).
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).
# When more than one visualizations are selected, one can klick and hold to zoom the spezified technique larger (Zooming). The others move to the background but stay visible.

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
* 28.11.2005 - Visualisation techniques are now selectable NEXT to one another (switches, no tables).
* 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
* 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
* 22.11.2005 - placement of first version of MICE tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-12-04T11:47:00Z

UE-InfoVis0506 0306110: /* Visual Mapping */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Stacked Barplot:''' The Actions in an intervall (e.g. 1 hour) are counted and represented as stacked bars. Moreover the different parts of the bars are connected with lines to give an idea what is the development from one intervall to the next.

# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Stacked Barplot====
# Bars represent actions in a time intervall (e.g. one hour).
# Space between bars leave room for printing lines to give an ide of the trend.
# Difference in colour represents the specific action (e.g. green = VIEW, red = BUY, etc.)
# Actions are stacked over each other, but with fixed order (VIEW before BUY before RATE etc.)



====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# When selecting more than 3 variables a subset out of htese variables is used to construct the scatterplot. An iteration takes place over time and exchanges variables continously to show all the selected variables.
# To make the graphic more readable the user can add color with respect to certain variables.

====Morphing / Dynamic Queries====
* Specify start and end point.
* Hit play to let morphing happen within the assigned timespan.
* Hit STOP to set the header to the starting time.
* Hit PAUSE to stop the header at any time.
* Drag and Drop the head to jump to a specific time.
* Switch between an absolute and relative time-axis.
* The start and end point are also giving the span for the dynamic queries applied on the active (selected) methods.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (dynamic queries).
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).
# When more than one visualizations are selected, one can klick and hold to zoom the spezified technique larger (Zooming). The others move to the background but stay visible.

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
* 28.11.2005 - Visualisation techniques are now selectable NEXT to one another (switches, no tables).
* 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
* 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
* 22.11.2005 - placement of first version of MICE tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-12-04T11:46:01Z

UE-InfoVis0506 0306110: /* Types of Visualization Applied */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Stacked Barplot:''' The Actions in an intervall (e.g. 1 hour) are counted and represented as stacked bars. Moreover the different parts of the bars are connected with lines to give an idea what is the development from one intervall to the next.

# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====History Flow====
# Lines represent a time intervall (e.g. one hour).
# Space between lines represent the change in user activity.
# Difference in colour represents the specific action (e.g. green = VIEW, red = BUY, etc.)
# Actions are stacked over each other, but with fixed order (VIEW before BUY before RATE etc.)
# Gaps represent a drop of an action after the intervall.



====Parallel Coordinates====

The following attributes should be a fix part of the chart:
# Product Type
# User Action
# maybe: Time from 0-24

The following attributes can additionally be added to the chart:
# Cluster
# Age of User
# Asset Type
# ...

Very important is that some attributes exclude themselves (like assets and tracks are disjunct groups). This is not a ''structure'' but defined.

It must be possible to add color for certain constraints to the data:
# The profiled and unprofiled user,
# the timespan of the actions,
# ...

Additionally:
# Brushing
# Linking

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# When selecting more than 3 variables a subset out of htese variables is used to construct the scatterplot. An iteration takes place over time and exchanges variables continously to show all the selected variables.
# To make the graphic more readable the user can add color with respect to certain variables.

====Morphing / Dynamic Queries====
* Specify start and end point.
* Hit play to let morphing happen within the assigned timespan.
* Hit STOP to set the header to the starting time.
* Hit PAUSE to stop the header at any time.
* Drag and Drop the head to jump to a specific time.
* Switch between an absolute and relative time-axis.
* The start and end point are also giving the span for the dynamic queries applied on the active (selected) methods.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (dynamic queries).
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).
# When more than one visualizations are selected, one can klick and hold to zoom the spezified technique larger (Zooming). The others move to the background but stay visible.

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
* 28.11.2005 - Visualisation techniques are now selectable NEXT to one another (switches, no tables).
* 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
* 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
* 22.11.2005 - placement of first version of MICE tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-12-04T11:39:55Z

UE-InfoVis0506 0306110:

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''History Flow:''' The Actions are represented through horizontal ''flowing'' bars. The thickness gives an Idea of the number of actions in an intervall.

# '''Parallel Coordinates:''' The Variables selected are normalized (to a range of 0-1) and plotted into a parallel coordinate plot. Techniques as Focus/Context must be appield to be able to figure out patterns.
# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====History Flow====
# Lines represent a time intervall (e.g. one hour).
# Space between lines represent the change in user activity.
# Difference in colour represents the specific action (e.g. green = VIEW, red = BUY, etc.)
# Actions are stacked over each other, but with fixed order (VIEW before BUY before RATE etc.)
# Gaps represent a drop of an action after the intervall.



====Parallel Coordinates====

The following attributes should be a fix part of the chart:
# Product Type
# User Action
# maybe: Time from 0-24

The following attributes can additionally be added to the chart:
# Cluster
# Age of User
# Asset Type
# ...

Very important is that some attributes exclude themselves (like assets and tracks are disjunct groups). This is not a ''structure'' but defined.

It must be possible to add color for certain constraints to the data:
# The profiled and unprofiled user,
# the timespan of the actions,
# ...

Additionally:
# Brushing
# Linking

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# When selecting more than 3 variables a subset out of htese variables is used to construct the scatterplot. An iteration takes place over time and exchanges variables continously to show all the selected variables.
# To make the graphic more readable the user can add color with respect to certain variables.

====Morphing / Dynamic Queries====
* Specify start and end point.
* Hit play to let morphing happen within the assigned timespan.
* Hit STOP to set the header to the starting time.
* Hit PAUSE to stop the header at any time.
* Drag and Drop the head to jump to a specific time.
* Switch between an absolute and relative time-axis.
* The start and end point are also giving the span for the dynamic queries applied on the active (selected) methods.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (dynamic queries).
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).
# When more than one visualizations are selected, one can klick and hold to zoom the spezified technique larger (Zooming). The others move to the background but stay visible.

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
* 28.11.2005 - Visualisation techniques are now selectable NEXT to one another (switches, no tables).
* 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
* 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
* 22.11.2005 - placement of first version of MICE tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-29T22:04:25Z

UE-InfoVis0506 0306110: /* Possibilities of Interaction */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Bubble Chart:''' Representing the parts of the online platform through bubbles. The traffic (or actions of people equal in the bubbles) is represented by the edges between the bubbles. Moreover the color inside the bubbles gives the proportions of persons as part of a selected group. A part stays grey representing "the rest".
# '''TreeNet:''' Showing the parts of the store (which are disjunct) as Treemaps, e.g. Tracks, Assets, Artists and dividing them into the different actions possible. The connection between the Treemaps (the actual netting) is then the amount of users which are equal within the parts. The edge is outlined with the smaller number of user giving the idea of ''the whole''.
# '''Parallel Coordinates:''' The Variables selected are normalized (to a range of 0-1) and plotted into a parallel coordinate plot. Techniques as Focus/Context must be appield to be able to figure out patterns.
# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Bubble Chart====
# The size of the bubble represents the number of actions taken within the part of the portal. Since the portal is semantically divided in parts in which you just can set actions on disjunct products, the different product types are also represented through this basic view.
# Different color segments of the bubble represent the (selected) type of actions.
# The position on the timeline represents all the actions taken until a special point of time. Moreover this can be regulated to show all actions in a special timespan.
# The "whole" is always visible through a very transparent bubblechart giving the idea of all the actions.
# The details to the segments and bubbles are becoming visible as Tooltip - text via the mousepointer when rolling over the segment or bubble.

<center>[[Image:BubbleSchema.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

<center>[[Image:BubbleConnSchema.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

<center>[[Image:BubbleSchemaFull.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

====TreeNet====
# The size of the single Treemaps is the number of user activities in a certain part of the portal.
# The dividing is done by the selected user activities.
# The edges between the single maps are representing the users which are equal in both maps. By that is is possible to see how much activity is done between two parts of the portal.
# This edges can also be divided with respect to certain variables (e.g. profiled and unprofiled user).
# Zooming into depth of one treemap or edge is provided, shrinking the context and enlarging the desired section.

<center>[[Image:TreeNetSchema.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

<center>[[Image:TreeNetFull.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

====Parallel Coordinates====

The following attributes should be a fix part of the chart:
# Product Type
# User Action
# maybe: Time from 0-24

The following attributes can additionally be added to the chart:
# Cluster
# Age of User
# Asset Type
# ...

Very important is that some attributes exclude themselves (like assets and tracks are disjunct groups). This is not a ''structure'' but defined.

It must be possible to add color for certain constraints to the data:
# The profiled and unprofiled user,
# the timespan of the actions,
# ...

Additionally:
# Brushing
# Linking

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# When selecting more than 3 variables a subset out of htese variables is used to construct the scatterplot. An iteration takes place over time and exchanges variables continously to show all the selected variables.
# To make the graphic more readable the user can add color with respect to certain variables.

====Morphing / Dynamic Queries====
* Specify start and end point.
* Hit play to let morphing happen within the assigned timespan.
* Hit STOP to set the header to the starting time.
* Hit PAUSE to stop the header at any time.
* Drag and Drop the head to jump to a specific time.
* Switch between an absolute and relative time-axis.
* The start and end point are also giving the span for the dynamic queries applied on the active (selected) methods.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (dynamic queries).
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).
# When more than one visualizations are selected, one can klick and hold to zoom the spezified technique larger (Zooming). The others move to the background but stay visible.

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
* 28.11.2005 - Visualisation techniques are now selectable NEXT to one another (switches, no tables).
* 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
* 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
* 22.11.2005 - placement of first version of MICE tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-29T22:02:56Z

UE-InfoVis0506 0306110: /* Event Log */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Bubble Chart:''' Representing the parts of the online platform through bubbles. The traffic (or actions of people equal in the bubbles) is represented by the edges between the bubbles. Moreover the color inside the bubbles gives the proportions of persons as part of a selected group. A part stays grey representing "the rest".
# '''TreeNet:''' Showing the parts of the store (which are disjunct) as Treemaps, e.g. Tracks, Assets, Artists and dividing them into the different actions possible. The connection between the Treemaps (the actual netting) is then the amount of users which are equal within the parts. The edge is outlined with the smaller number of user giving the idea of ''the whole''.
# '''Parallel Coordinates:''' The Variables selected are normalized (to a range of 0-1) and plotted into a parallel coordinate plot. Techniques as Focus/Context must be appield to be able to figure out patterns.
# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Bubble Chart====
# The size of the bubble represents the number of actions taken within the part of the portal. Since the portal is semantically divided in parts in which you just can set actions on disjunct products, the different product types are also represented through this basic view.
# Different color segments of the bubble represent the (selected) type of actions.
# The position on the timeline represents all the actions taken until a special point of time. Moreover this can be regulated to show all actions in a special timespan.
# The "whole" is always visible through a very transparent bubblechart giving the idea of all the actions.
# The details to the segments and bubbles are becoming visible as Tooltip - text via the mousepointer when rolling over the segment or bubble.

<center>[[Image:BubbleSchema.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

<center>[[Image:BubbleConnSchema.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

<center>[[Image:BubbleSchemaFull.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

====TreeNet====
# The size of the single Treemaps is the number of user activities in a certain part of the portal.
# The dividing is done by the selected user activities.
# The edges between the single maps are representing the users which are equal in both maps. By that is is possible to see how much activity is done between two parts of the portal.
# This edges can also be divided with respect to certain variables (e.g. profiled and unprofiled user).
# Zooming into depth of one treemap or edge is provided, shrinking the context and enlarging the desired section.

<center>[[Image:TreeNetSchema.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

<center>[[Image:TreeNetFull.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

====Parallel Coordinates====

The following attributes should be a fix part of the chart:
# Product Type
# User Action
# maybe: Time from 0-24

The following attributes can additionally be added to the chart:
# Cluster
# Age of User
# Asset Type
# ...

Very important is that some attributes exclude themselves (like assets and tracks are disjunct groups). This is not a ''structure'' but defined.

It must be possible to add color for certain constraints to the data:
# The profiled and unprofiled user,
# the timespan of the actions,
# ...

Additionally:
# Brushing
# Linking

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# When selecting more than 3 variables a subset out of htese variables is used to construct the scatterplot. An iteration takes place over time and exchanges variables continously to show all the selected variables.
# To make the graphic more readable the user can add color with respect to certain variables.

====Morphing / Dynamic Queries====
* Specify start and end point.
* Hit play to let morphing happen within the assigned timespan.
* Hit STOP to set the header to the starting time.
* Hit PAUSE to stop the header at any time.
* Drag and Drop the head to jump to a specific time.
* Switch between an absolute and relative time-axis.
* The start and end point are also giving the span for the dynamic queries applied on the active (selected) methods.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (dynamic queries).
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
* 28.11.2005 - Visualisation techniques are now selectable NEXT to one another (switches, no tables).
* 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
* 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
* 22.11.2005 - placement of first version of MICE tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching talk:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-26T19:05:23Z

UE-InfoVis0506 0306110:

'''Kommentar zum Konzept:'''

*Der Entwurf und Designvorschlag sind schon recht detailiert - super!
*Was bedeutet das Attribut "homeCluster"?
*Ihr beschreibt 2 Benutzergruppen mit veschiedenen Anforderungen - es wäre vielleicht besser sich auf einen Typ zu konzentrieren.
*Referenzen bzw. Weblinks fehlen bei "Known Solutions"
*Viele Fehler im Text
*Das Mantra lautet: Overview First - Zoom and Filter (nicht Zoom and Focus)
*Die Beschreibung der TreeNet Visualisierung ist nicht klar beschrieben.
*Ihr verwendet mehrere verschiedene interaktive Techniken - sehr gut.
*Was aber evt. auch Sinn macht, ist eine gleichzeitige Darstellung von mehreren Techniken nebeneinander - um verschiedene Aspekte untersuchen zu können. (Bei eurem Proposal kann man jeweils nur eine Methode betrachten)

-- [[User:Iwolf|Wolfgang Aigner]] 14:32, 25 November 2005 (CET)

'''Corrections and Comments'''
*Gone through writing, corrected mantra- Stefan Schnabl, 25.11.2005
*The attribute "homeClusters" is alwas NULL in the table "album", hence it has no active meaning for us. - Stefan Schnabl, 25.11.2005
*Both groups can gain knowledge out of the visualisation applied. E.g. owners see where is a lot of traffic, developers as well. This information is usefull for both. They can as well see the relative amout of profiled users vs. unprofiled ones. This can lead to interesting hypothesis for developers as well as for owners. (e.g. owners might think that the profiled users are more active, and by that send them mor special hints and newsletters, on the other hand, if for instance the amount of unprofiled users is very low it can indicate the need for a more useful entry mask for getting a profiled user). - Stefan Schnabl, 25.11.2005
*With all due respect but I dont understand what is unclear about the TreeNet technique. I ask for specific question you want us to answer additionally. - Stefan Schnabl, 25.11.2005
:'''''--> A graphic would help a lot (Most importantly, I would like to see a clarification of the differences between TreeNet and Bubble Chart)''''' -- [[User:Iwolf|Wolfgang Aigner]] 12:56, 26 November 2005 (CET)

* Graphics for BubbleChart and TreeNet added. I hope this makes the technique clearer. - Stefan Schnabl, 26.11.2005
* The differences between Bubble Chart and Tree Net are marginal. Disjunct groups can better be seperated via the BubbleChart since they are represented through the belts. E.g. one can leave space between them. - Stefan Schnabl, 26.11.2005
* The idea of the TreeNet evolved out of the BubbleChart. The underlying computation is quite similar so we decided to not drop one out of the concept. - Stefan Schnabl, 26.11.2005

Teaching talk:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-26T19:05:13Z

UE-InfoVis0506 0306110:

'''Kommentar zum Konzept:'''

*Der Entwurf und Designvorschlag sind schon recht detailiert - super!
*Was bedeutet das Attribut "homeCluster"?
*Ihr beschreibt 2 Benutzergruppen mit veschiedenen Anforderungen - es wäre vielleicht besser sich auf einen Typ zu konzentrieren.
*Referenzen bzw. Weblinks fehlen bei "Known Solutions"
*Viele Fehler im Text
*Das Mantra lautet: Overview First - Zoom and Filter (nicht Zoom and Focus)
*Die Beschreibung der TreeNet Visualisierung ist nicht klar beschrieben.
*Ihr verwendet mehrere verschiedene interaktive Techniken - sehr gut.
*Was aber evt. auch Sinn macht, ist eine gleichzeitige Darstellung von mehreren Techniken nebeneinander - um verschiedene Aspekte untersuchen zu können. (Bei eurem Proposal kann man jeweils nur eine Methode betrachten)

-- [[User:Iwolf|Wolfgang Aigner]] 14:32, 25 November 2005 (CET)

'''Corrections and Comments'''
*Gone through writing, corrected mantra- Stefan Schnabl, 25.11.2005
*The attribute "homeClusters" is alwas NULL in the table "album", hence it has no active meaning for us. - Stefan Schnabl, 25.11.2005
*Both groups can gain knowledge out of the visualisation applied. E.g. owners see where is a lot of traffic, developers as well. This information is usefull for both. They can as well see the relative amout of profiled users vs. unprofiled ones. This can lead to interesting hypothesis for developers as well as for owners. (e.g. owners might think that the profiled users are more active, and by that send them mor special hints and newsletters, on the other hand, if for instance the amount of unprofiled users is very low it can indicate the need for a more useful entry mask for getting a profiled user). - Stefan Schnabl, 25.11.2005
*With all due respect but I dont understand what is unclear about the TreeNet technique. I ask for specific question you want us to answer additionally. - Stefan Schnabl, 25.11.2005
:'''''--> A graphic would help a lot (Most importantly, I would like to see a clarification of the differences between TreeNet and Bubble Chart)''''' -- [[User:Iwolf|Wolfgang Aigner]] 12:56, 26 November 2005 (CET)

* Graphic for BubbleChart and TreeNet added. I hope this makes the technique clearer. - Stefan Schnabl, 26.11.2005
* The differences between Bubble Chart and Tree Net are marginal. Disjunct groups can better be seperated via the BubbleChart since they are represented through the belts. E.g. one can leave space between them. - Stefan Schnabl, 26.11.2005
* The idea of the TreeNet evolved out of the BubbleChart. The underlying computation is quite similar so we decided to not drop one out of the concept. - Stefan Schnabl, 26.11.2005

File:TreeNetFull.jpg

2005-11-26T19:01:24Z

UE-InfoVis0506 0306110: Example for a possible TreeNet.

== Summary ==
Example for a possible TreeNet.
== Copyright status ==
[Schnabl, Seyfang, Fritz, 2005]
== Source ==

File:TreeNetSchema.jpg

2005-11-26T19:00:58Z

UE-InfoVis0506 0306110: Schema of the TreeNet technique for database structured data.

== Summary ==
Schema of the TreeNet technique for database structured data.
== Copyright status ==
[Schnabl, Seyfang, Fritz, 2005]
== Source ==

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-26T19:00:28Z

UE-InfoVis0506 0306110: /* TreeNet */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Bubble Chart:''' Representing the parts of the online platform through bubbles. The traffic (or actions of people equal in the bubbles) is represented by the edges between the bubbles. Moreover the color inside the bubbles gives the proportions of persons as part of a selected group. A part stays grey representing "the rest".
# '''TreeNet:''' Showing the parts of the store (which are disjunct) as Treemaps, e.g. Tracks, Assets, Artists and dividing them into the different actions possible. The connection between the Treemaps (the actual netting) is then the amount of users which are equal within the parts. The edge is outlined with the smaller number of user giving the idea of ''the whole''.
# '''Parallel Coordinates:''' The Variables selected are normalized (to a range of 0-1) and plotted into a parallel coordinate plot. Techniques as Focus/Context must be appield to be able to figure out patterns.
# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Bubble Chart====
# The size of the bubble represents the number of actions taken within the part of the portal. Since the portal is semantically divided in parts in which you just can set actions on disjunct products, the different product types are also represented through this basic view.
# Different color segments of the bubble represent the (selected) type of actions.
# The position on the timeline represents all the actions taken until a special point of time. Moreover this can be regulated to show all actions in a special timespan.
# The "whole" is always visible through a very transparent bubblechart giving the idea of all the actions.
# The details to the segments and bubbles are becoming visible as Tooltip - text via the mousepointer when rolling over the segment or bubble.

<center>[[Image:BubbleSchema.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

<center>[[Image:BubbleConnSchema.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

<center>[[Image:BubbleSchemaFull.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

====TreeNet====
# The size of the single Treemaps is the number of user activities in a certain part of the portal.
# The dividing is done by the selected user activities.
# The edges between the single maps are representing the users which are equal in both maps. By that is is possible to see how much activity is done between two parts of the portal.
# This edges can also be divided with respect to certain variables (e.g. profiled and unprofiled user).
# Zooming into depth of one treemap or edge is provided, shrinking the context and enlarging the desired section.

<center>[[Image:TreeNetSchema.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

<center>[[Image:TreeNetFull.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

====Parallel Coordinates====

The following attributes should be a fix part of the chart:
# Product Type
# User Action
# maybe: Time from 0-24

The following attributes can additionally be added to the chart:
# Cluster
# Age of User
# Asset Type
# ...

Very important is that some attributes exclude themselves (like assets and tracks are disjunct groups). This is not a ''structure'' but defined.

It must be possible to add color for certain constraints to the data:
# The profiled and unprofiled user,
# the timespan of the actions,
# ...

Additionally:
# Brushing
# Linking

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# When selecting more than 3 variables a subset out of htese variables is used to construct the scatterplot. An iteration takes place over time and exchanges variables continously to show all the selected variables.
# To make the graphic more readable the user can add color with respect to certain variables.

====Morphing / Dynamic Queries====
* Specify start and end point.
* Hit play to let morphing happen within the assigned timespan.
* Hit STOP to set the header to the starting time.
* Hit PAUSE to stop the header at any time.
* Drag and Drop the head to jump to a specific time.
* Switch between an absolute and relative time-axis.
* The start and end point are also giving the span for the dynamic queries applied on the active (selected) methods.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (dynamic queries).
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
* 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
* 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
* 22.11.2005 - placement of first version of MICE tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

File:BubbleSchemaFull.jpg

2005-11-26T18:49:02Z

UE-InfoVis0506 0306110: Full example of the Bubble Chart technique.

== Summary ==
Full example of the Bubble Chart technique.
== Copyright status ==
[Schnabl, Seyfang, Fritz, 2005]
== Source ==

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-26T18:48:36Z

UE-InfoVis0506 0306110: /* Bubble Chart */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Bubble Chart:''' Representing the parts of the online platform through bubbles. The traffic (or actions of people equal in the bubbles) is represented by the edges between the bubbles. Moreover the color inside the bubbles gives the proportions of persons as part of a selected group. A part stays grey representing "the rest".
# '''TreeNet:''' Showing the parts of the store (which are disjunct) as Treemaps, e.g. Tracks, Assets, Artists and dividing them into the different actions possible. The connection between the Treemaps (the actual netting) is then the amount of users which are equal within the parts. The edge is outlined with the smaller number of user giving the idea of ''the whole''.
# '''Parallel Coordinates:''' The Variables selected are normalized (to a range of 0-1) and plotted into a parallel coordinate plot. Techniques as Focus/Context must be appield to be able to figure out patterns.
# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Bubble Chart====
# The size of the bubble represents the number of actions taken within the part of the portal. Since the portal is semantically divided in parts in which you just can set actions on disjunct products, the different product types are also represented through this basic view.
# Different color segments of the bubble represent the (selected) type of actions.
# The position on the timeline represents all the actions taken until a special point of time. Moreover this can be regulated to show all actions in a special timespan.
# The "whole" is always visible through a very transparent bubblechart giving the idea of all the actions.
# The details to the segments and bubbles are becoming visible as Tooltip - text via the mousepointer when rolling over the segment or bubble.

<center>[[Image:BubbleSchema.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

<center>[[Image:BubbleConnSchema.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

<center>[[Image:BubbleSchemaFull.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

====TreeNet====
# The size of the single Treemaps is the number of user activities in a certain part of the portal.
# The dividing is done by the selected user activities.
# The edges between the single maps are representing the users which are equal in both maps. By that is is possible to see how much activity is done between two parts of the portal.
# This edges can also be divided with respect to certain variables (e.g. profiled and unprofiled user).
# Zooming into depth of one treemap or edge is provided, shrinking the context and enlarging the desired section.

====Parallel Coordinates====

The following attributes should be a fix part of the chart:
# Product Type
# User Action
# maybe: Time from 0-24

The following attributes can additionally be added to the chart:
# Cluster
# Age of User
# Asset Type
# ...

Very important is that some attributes exclude themselves (like assets and tracks are disjunct groups). This is not a ''structure'' but defined.

It must be possible to add color for certain constraints to the data:
# The profiled and unprofiled user,
# the timespan of the actions,
# ...

Additionally:
# Brushing
# Linking

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# When selecting more than 3 variables a subset out of htese variables is used to construct the scatterplot. An iteration takes place over time and exchanges variables continously to show all the selected variables.
# To make the graphic more readable the user can add color with respect to certain variables.

====Morphing / Dynamic Queries====
* Specify start and end point.
* Hit play to let morphing happen within the assigned timespan.
* Hit STOP to set the header to the starting time.
* Hit PAUSE to stop the header at any time.
* Drag and Drop the head to jump to a specific time.
* Switch between an absolute and relative time-axis.
* The start and end point are also giving the span for the dynamic queries applied on the active (selected) methods.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (dynamic queries).
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
* 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
* 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
* 22.11.2005 - placement of first version of MICE tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-26T18:44:04Z

UE-InfoVis0506 0306110: /* Bubble Chart */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Bubble Chart:''' Representing the parts of the online platform through bubbles. The traffic (or actions of people equal in the bubbles) is represented by the edges between the bubbles. Moreover the color inside the bubbles gives the proportions of persons as part of a selected group. A part stays grey representing "the rest".
# '''TreeNet:''' Showing the parts of the store (which are disjunct) as Treemaps, e.g. Tracks, Assets, Artists and dividing them into the different actions possible. The connection between the Treemaps (the actual netting) is then the amount of users which are equal within the parts. The edge is outlined with the smaller number of user giving the idea of ''the whole''.
# '''Parallel Coordinates:''' The Variables selected are normalized (to a range of 0-1) and plotted into a parallel coordinate plot. Techniques as Focus/Context must be appield to be able to figure out patterns.
# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Bubble Chart====
# The size of the bubble represents the number of actions taken within the part of the portal. Since the portal is semantically divided in parts in which you just can set actions on disjunct products, the different product types are also represented through this basic view.
# Different color segments of the bubble represent the (selected) type of actions.
# The position on the timeline represents all the actions taken until a special point of time. Moreover this can be regulated to show all actions in a special timespan.
# The "whole" is always visible through a very transparent bubblechart giving the idea of all the actions.
# The details to the segments and bubbles are becoming visible as Tooltip - text via the mousepointer when rolling over the segment or bubble.

<center>[[Image:BubbleSchema.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

<center>[[Image:BubbleConnSchema.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

====TreeNet====
# The size of the single Treemaps is the number of user activities in a certain part of the portal.
# The dividing is done by the selected user activities.
# The edges between the single maps are representing the users which are equal in both maps. By that is is possible to see how much activity is done between two parts of the portal.
# This edges can also be divided with respect to certain variables (e.g. profiled and unprofiled user).
# Zooming into depth of one treemap or edge is provided, shrinking the context and enlarging the desired section.

====Parallel Coordinates====

The following attributes should be a fix part of the chart:
# Product Type
# User Action
# maybe: Time from 0-24

The following attributes can additionally be added to the chart:
# Cluster
# Age of User
# Asset Type
# ...

Very important is that some attributes exclude themselves (like assets and tracks are disjunct groups). This is not a ''structure'' but defined.

It must be possible to add color for certain constraints to the data:
# The profiled and unprofiled user,
# the timespan of the actions,
# ...

Additionally:
# Brushing
# Linking

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# When selecting more than 3 variables a subset out of htese variables is used to construct the scatterplot. An iteration takes place over time and exchanges variables continously to show all the selected variables.
# To make the graphic more readable the user can add color with respect to certain variables.

====Morphing / Dynamic Queries====
* Specify start and end point.
* Hit play to let morphing happen within the assigned timespan.
* Hit STOP to set the header to the starting time.
* Hit PAUSE to stop the header at any time.
* Drag and Drop the head to jump to a specific time.
* Switch between an absolute and relative time-axis.
* The start and end point are also giving the span for the dynamic queries applied on the active (selected) methods.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (dynamic queries).
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
* 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
* 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
* 22.11.2005 - placement of first version of MICE tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-26T18:43:46Z

UE-InfoVis0506 0306110: /* Bubble Chart */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Bubble Chart:''' Representing the parts of the online platform through bubbles. The traffic (or actions of people equal in the bubbles) is represented by the edges between the bubbles. Moreover the color inside the bubbles gives the proportions of persons as part of a selected group. A part stays grey representing "the rest".
# '''TreeNet:''' Showing the parts of the store (which are disjunct) as Treemaps, e.g. Tracks, Assets, Artists and dividing them into the different actions possible. The connection between the Treemaps (the actual netting) is then the amount of users which are equal within the parts. The edge is outlined with the smaller number of user giving the idea of ''the whole''.
# '''Parallel Coordinates:''' The Variables selected are normalized (to a range of 0-1) and plotted into a parallel coordinate plot. Techniques as Focus/Context must be appield to be able to figure out patterns.
# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Bubble Chart====
# The size of the bubble represents the number of actions taken within the part of the portal. Since the portal is semantically divided in parts in which you just can set actions on disjunct products, the different product types are also represented through this basic view.
# Different color segments of the bubble represent the (selected) type of actions.
# The position on the timeline represents all the actions taken until a special point of time. Moreover this can be regulated to show all actions in a special timespan.
# The "whole" is always visible through a very transparent bubblechart giving the idea of all the actions.
# The details to the segments and bubbles are becoming visible as Tiptext via the mousepointer when rolling over the segment or bubble.

<center>[[Image:BubbleSchema.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

<center>[[Image:BubbleConnSchema.jpg]] [Schnabl, Seyfang, Fritz, 2005]</center>

====TreeNet====
# The size of the single Treemaps is the number of user activities in a certain part of the portal.
# The dividing is done by the selected user activities.
# The edges between the single maps are representing the users which are equal in both maps. By that is is possible to see how much activity is done between two parts of the portal.
# This edges can also be divided with respect to certain variables (e.g. profiled and unprofiled user).
# Zooming into depth of one treemap or edge is provided, shrinking the context and enlarging the desired section.

====Parallel Coordinates====

The following attributes should be a fix part of the chart:
# Product Type
# User Action
# maybe: Time from 0-24

The following attributes can additionally be added to the chart:
# Cluster
# Age of User
# Asset Type
# ...

Very important is that some attributes exclude themselves (like assets and tracks are disjunct groups). This is not a ''structure'' but defined.

It must be possible to add color for certain constraints to the data:
# The profiled and unprofiled user,
# the timespan of the actions,
# ...

Additionally:
# Brushing
# Linking

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# When selecting more than 3 variables a subset out of htese variables is used to construct the scatterplot. An iteration takes place over time and exchanges variables continously to show all the selected variables.
# To make the graphic more readable the user can add color with respect to certain variables.

====Morphing / Dynamic Queries====
* Specify start and end point.
* Hit play to let morphing happen within the assigned timespan.
* Hit STOP to set the header to the starting time.
* Hit PAUSE to stop the header at any time.
* Drag and Drop the head to jump to a specific time.
* Switch between an absolute and relative time-axis.
* The start and end point are also giving the span for the dynamic queries applied on the active (selected) methods.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (dynamic queries).
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
* 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
* 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
* 22.11.2005 - placement of first version of MICE tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

File:BubbleConnSchema.jpg

2005-11-26T18:43:06Z

UE-InfoVis0506 0306110: Schema of the connection parts of the Bubble Chart technique.

== Summary ==
Schema of the connection parts of the Bubble Chart technique.
== Copyright status ==
[Schnabl, Seyfang, Fritz, 2005]
== Source ==

File:BubbleSchema.jpg

2005-11-26T18:42:28Z

UE-InfoVis0506 0306110: Schema of the Bubble Chart technique for visualizing datastructures modelled with ER diagramms.

== Summary ==
Schema of the Bubble Chart technique for visualizing datastructures modelled with ER diagramms.
== Copyright status ==
[Schnabl, Seyfang, Fritz, 2005]
== Source ==

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-26T18:40:49Z

UE-InfoVis0506 0306110: /* Bubble Chart */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Bubble Chart:''' Representing the parts of the online platform through bubbles. The traffic (or actions of people equal in the bubbles) is represented by the edges between the bubbles. Moreover the color inside the bubbles gives the proportions of persons as part of a selected group. A part stays grey representing "the rest".
# '''TreeNet:''' Showing the parts of the store (which are disjunct) as Treemaps, e.g. Tracks, Assets, Artists and dividing them into the different actions possible. The connection between the Treemaps (the actual netting) is then the amount of users which are equal within the parts. The edge is outlined with the smaller number of user giving the idea of ''the whole''.
# '''Parallel Coordinates:''' The Variables selected are normalized (to a range of 0-1) and plotted into a parallel coordinate plot. Techniques as Focus/Context must be appield to be able to figure out patterns.
# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Bubble Chart====
# The size of the bubble represents the number of actions taken within the part of the portal. Since the portal is semantically divided in parts in which you just can set actions on disjunct products, the different product types are also represented through this basic view.
# Different color segments of the bubble represent the (selected) type of actions.
# The position on the timeline represents all the actions taken until a special point of time. Moreover this can be regulated to show all actions in a special timespan.
# The "whole" is always visible through a very transparent bubblechart giving the idea of all the actions.
# The details to the segments and bubbles are becoming visible as Tiptext via the mousepointer when rolling over the segment or bubble.

[[Image:BubbleSchema.jpg]] [Schnabl, Seyfang, Fritz, 2005]

[[Image:BubbleConnSchema.jpg]] [Schnabl, Seyfang, Fritz, 2005]

====TreeNet====
# The size of the single Treemaps is the number of user activities in a certain part of the portal.
# The dividing is done by the selected user activities.
# The edges between the single maps are representing the users which are equal in both maps. By that is is possible to see how much activity is done between two parts of the portal.
# This edges can also be divided with respect to certain variables (e.g. profiled and unprofiled user).
# Zooming into depth of one treemap or edge is provided, shrinking the context and enlarging the desired section.

====Parallel Coordinates====

The following attributes should be a fix part of the chart:
# Product Type
# User Action
# maybe: Time from 0-24

The following attributes can additionally be added to the chart:
# Cluster
# Age of User
# Asset Type
# ...

Very important is that some attributes exclude themselves (like assets and tracks are disjunct groups). This is not a ''structure'' but defined.

It must be possible to add color for certain constraints to the data:
# The profiled and unprofiled user,
# the timespan of the actions,
# ...

Additionally:
# Brushing
# Linking

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# When selecting more than 3 variables a subset out of htese variables is used to construct the scatterplot. An iteration takes place over time and exchanges variables continously to show all the selected variables.
# To make the graphic more readable the user can add color with respect to certain variables.

====Morphing / Dynamic Queries====
* Specify start and end point.
* Hit play to let morphing happen within the assigned timespan.
* Hit STOP to set the header to the starting time.
* Hit PAUSE to stop the header at any time.
* Drag and Drop the head to jump to a specific time.
* Switch between an absolute and relative time-axis.
* The start and end point are also giving the span for the dynamic queries applied on the active (selected) methods.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (dynamic queries).
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
* 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
* 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
* 22.11.2005 - placement of first version of MICE tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching talk:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-26T09:29:17Z

UE-InfoVis0506 0306110:

Teaching talk:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-26T09:28:50Z

UE-InfoVis0506 0306110:

Teaching talk:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-26T09:28:36Z

UE-InfoVis0506 0306110:

Teaching talk:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-26T09:28:21Z

UE-InfoVis0506 0306110:

'''Kommentar zum Konzept:'''

*Der Entwurf und Designvorschlag sind schon recht detailiert - super!
*Was bedeutet das Attribut "homeCluster"?
*Ihr beschreibt 2 Benutzergruppen mit veschiedenen Anforderungen - es wäre vielleicht besser sich auf einen Typ zu konzentrieren.
*Referenzen bzw. Weblinks fehlen bei "Known Solutions"
*Viele Fehler im Text
*Das Mantra lautet: Overview First - Zoom and Filter (nicht Zoom and Focus)
*Die Beschreibung der TreeNet Visualisierung ist nicht klar beschrieben.
*Ihr verwendet mehrere verschiedene interaktive Techniken - sehr gut.
*Was aber evt. auch Sinn macht, ist eine gleichzeitige Darstellung von mehreren Techniken nebeneinander - um verschiedene Aspekte untersuchen zu können. (Bei eurem Proposal kann man jeweils nur eine Methode betrachten)

-- [[User:Iwolf|Wolfgang Aigner]] 14:32, 25 November 2005 (CET)

'''Korrekturen am Artikel und Statements zur Beurteilung'''
*Gone through writing, corrected mantra- Stefan Schnabl, 25.11.2005
*The attribute "homeClusters" is alwas NULL in the table "album", hence has no active meaning for our usage. - Stefan Schnabl, 25.11.2005
*Both groups can gain knowledge out of the visualisation applied. E.g. owners see where is a lot of traffic, developers as well. This information is usefull for both. They can as well see the relative amout of profiled users vs. unprofiled ones. This can lead to interesting hypothesis for developers as well as for owners. (e.g. owners might think that the profiled users are more active, and by that send them mor special hints and newsletters, on the other hand, if for instance the amount of unprofiled users is very low it can indicate the need for a more useful entry mask for getting a profiled user). - Stefan Schnabl, 25.11.2005
*With all due respect but I dont understand what is unclear about the TreeNet technique. - Stefan Schnabl, 25.11.2005

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-25T22:56:11Z

UE-InfoVis0506 0306110: /* Possibilities of Interaction */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Bubble Chart:''' Representing the parts of the online platform through bubbles. The traffic (or actions of people equal in the bubbles) is represented by the edges between the bubbles. Moreover the color inside the bubbles gives the proportions of persons as part of a selected group. A part stays grey representing "the rest".
# '''TreeNet:''' Showing the parts of the store (which are disjunct) as Treemaps, e.g. Tracks, Assets, Artists and dividing them into the different actions possible. The connection between the Treemaps (the actual netting) is then the amount of users which are equal within the parts. The edge is outlined with the smaller number of user giving the idea of ''the whole''.
# '''Parallel Coordinates:''' The Variables selected are normalized (to a range of 0-1) and plotted into a parallel coordinate plot. Techniques as Focus/Context must be appield to be able to figure out patterns.
# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Bubble Chart====
# The size of the bubble represents the number of actions taken within the part of the portal. Since the portal is semantically divided in parts in which you just can set actions on disjunct products, the different product types are also represented through this basic view.
# Different color segments of the bubble represent the (selected) type of actions.
# The position on the timeline represents all the actions taken until a special point of time. Moreover this can be regulated to show all actions in a special timespan.
# The "whole" is always visible through a very transparent bubblechart giving the idea of all the actions.
# The details to the segments and bubbles are becoming visible as Tiptext via the mousepointer when rolling over the segment or bubble.

====TreeNet====
# The size of the single Treemaps is the number of user activities in a certain part of the portal.
# The dividing is done by the selected user activities.
# The edges between the single maps are representing the users which are equal in both maps. By that is is possible to see how much activity is done between two parts of the portal.
# This edges can also be divided with respect to certain variables (e.g. profiled and unprofiled user).
# Zooming into depth of one treemap or edge is provided, shrinking the context and enlarging the desired section.

====Parallel Coordinates====

The following attributes should be a fix part of the chart:
# Product Type
# User Action
# maybe: Time from 0-24

The following attributes can additionally be added to the chart:
# Cluster
# Age of User
# Asset Type
# ...

Very important is that some attributes exclude themselves (like assets and tracks are disjunct groups). This is not a ''structure'' but defined.

It must be possible to add color for certain constraints to the data:
# The profiled and unprofiled user,
# the timespan of the actions,
# ...

Additionally:
# Brushing
# Linking

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# When selecting more than 3 variables a subset out of htese variables is used to construct the scatterplot. An iteration takes place over time and exchanges variables continously to show all the selected variables.
# To make the graphic more readable the user can add color with respect to certain variables.

====Morphing / Dynamic Queries====
* Specify start and end point.
* Hit play to let morphing happen within the assigned timespan.
* Hit STOP to set the header to the starting time.
* Hit PAUSE to stop the header at any time.
* Drag and Drop the head to jump to a specific time.
* Switch between an absolute and relative time-axis.
* The start and end point are also giving the span for the dynamic queries applied on the active (selected) methods.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (dynamic queries).
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
* 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
* 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
* 22.11.2005 - placement of first version of MICE tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-25T22:56:02Z

UE-InfoVis0506 0306110: /* Possibilities of Interaction */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Bubble Chart:''' Representing the parts of the online platform through bubbles. The traffic (or actions of people equal in the bubbles) is represented by the edges between the bubbles. Moreover the color inside the bubbles gives the proportions of persons as part of a selected group. A part stays grey representing "the rest".
# '''TreeNet:''' Showing the parts of the store (which are disjunct) as Treemaps, e.g. Tracks, Assets, Artists and dividing them into the different actions possible. The connection between the Treemaps (the actual netting) is then the amount of users which are equal within the parts. The edge is outlined with the smaller number of user giving the idea of ''the whole''.
# '''Parallel Coordinates:''' The Variables selected are normalized (to a range of 0-1) and plotted into a parallel coordinate plot. Techniques as Focus/Context must be appield to be able to figure out patterns.
# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Bubble Chart====
# The size of the bubble represents the number of actions taken within the part of the portal. Since the portal is semantically divided in parts in which you just can set actions on disjunct products, the different product types are also represented through this basic view.
# Different color segments of the bubble represent the (selected) type of actions.
# The position on the timeline represents all the actions taken until a special point of time. Moreover this can be regulated to show all actions in a special timespan.
# The "whole" is always visible through a very transparent bubblechart giving the idea of all the actions.
# The details to the segments and bubbles are becoming visible as Tiptext via the mousepointer when rolling over the segment or bubble.

====TreeNet====
# The size of the single Treemaps is the number of user activities in a certain part of the portal.
# The dividing is done by the selected user activities.
# The edges between the single maps are representing the users which are equal in both maps. By that is is possible to see how much activity is done between two parts of the portal.
# This edges can also be divided with respect to certain variables (e.g. profiled and unprofiled user).
# Zooming into depth of one treemap or edge is provided, shrinking the context and enlarging the desired section.

====Parallel Coordinates====

The following attributes should be a fix part of the chart:
# Product Type
# User Action
# maybe: Time from 0-24

The following attributes can additionally be added to the chart:
# Cluster
# Age of User
# Asset Type
# ...

Very important is that some attributes exclude themselves (like assets and tracks are disjunct groups). This is not a ''structure'' but defined.

It must be possible to add color for certain constraints to the data:
# The profiled and unprofiled user,
# the timespan of the actions,
# ...

Additionally:
# Brushing
# Linking

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# When selecting more than 3 variables a subset out of htese variables is used to construct the scatterplot. An iteration takes place over time and exchanges variables continously to show all the selected variables.
# To make the graphic more readable the user can add color with respect to certain variables.

====Morphing / Dynamic Queries====
* Specify start and end point.
* Hit play to let morphing happen within the assigned timespan.
* Hit STOP to set the header to the starting time.
* Hit PAUSE to stop the header at any time.
* Drag and Drop the head to jump to a specific time.
* Switch between an absolute and relative time-axis.
* The start and end point are also giving the span for the dynamic queries applied on the active (selected) methods.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (dynamic queries).
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When Making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
* 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
* 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
* 22.11.2005 - placement of first version of MICE tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-25T22:55:44Z

UE-InfoVis0506 0306110: /* Morphing */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Bubble Chart:''' Representing the parts of the online platform through bubbles. The traffic (or actions of people equal in the bubbles) is represented by the edges between the bubbles. Moreover the color inside the bubbles gives the proportions of persons as part of a selected group. A part stays grey representing "the rest".
# '''TreeNet:''' Showing the parts of the store (which are disjunct) as Treemaps, e.g. Tracks, Assets, Artists and dividing them into the different actions possible. The connection between the Treemaps (the actual netting) is then the amount of users which are equal within the parts. The edge is outlined with the smaller number of user giving the idea of ''the whole''.
# '''Parallel Coordinates:''' The Variables selected are normalized (to a range of 0-1) and plotted into a parallel coordinate plot. Techniques as Focus/Context must be appield to be able to figure out patterns.
# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Bubble Chart====
# The size of the bubble represents the number of actions taken within the part of the portal. Since the portal is semantically divided in parts in which you just can set actions on disjunct products, the different product types are also represented through this basic view.
# Different color segments of the bubble represent the (selected) type of actions.
# The position on the timeline represents all the actions taken until a special point of time. Moreover this can be regulated to show all actions in a special timespan.
# The "whole" is always visible through a very transparent bubblechart giving the idea of all the actions.
# The details to the segments and bubbles are becoming visible as Tiptext via the mousepointer when rolling over the segment or bubble.

====TreeNet====
# The size of the single Treemaps is the number of user activities in a certain part of the portal.
# The dividing is done by the selected user activities.
# The edges between the single maps are representing the users which are equal in both maps. By that is is possible to see how much activity is done between two parts of the portal.
# This edges can also be divided with respect to certain variables (e.g. profiled and unprofiled user).
# Zooming into depth of one treemap or edge is provided, shrinking the context and enlarging the desired section.

====Parallel Coordinates====

The following attributes should be a fix part of the chart:
# Product Type
# User Action
# maybe: Time from 0-24

The following attributes can additionally be added to the chart:
# Cluster
# Age of User
# Asset Type
# ...

Very important is that some attributes exclude themselves (like assets and tracks are disjunct groups). This is not a ''structure'' but defined.

It must be possible to add color for certain constraints to the data:
# The profiled and unprofiled user,
# the timespan of the actions,
# ...

Additionally:
# Brushing
# Linking

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# When selecting more than 3 variables a subset out of htese variables is used to construct the scatterplot. An iteration takes place over time and exchanges variables continously to show all the selected variables.
# To make the graphic more readable the user can add color with respect to certain variables.

====Morphing / Dynamic Queries====
* Specify start and end point.
* Hit play to let morphing happen within the assigned timespan.
* Hit STOP to set the header to the starting time.
* Hit PAUSE to stop the header at any time.
* Drag and Drop the head to jump to a specific time.
* Switch between an absolute and relative time-axis.
* The start and end point are also giving the span for the dynamic queries applied on the active (selected) methods.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (Focus -Context)
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When Making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
* 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
* 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
* 22.11.2005 - placement of first version of MICE tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-25T22:54:45Z

UE-InfoVis0506 0306110: /* (multidimensional) Scatterplot */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Bubble Chart:''' Representing the parts of the online platform through bubbles. The traffic (or actions of people equal in the bubbles) is represented by the edges between the bubbles. Moreover the color inside the bubbles gives the proportions of persons as part of a selected group. A part stays grey representing "the rest".
# '''TreeNet:''' Showing the parts of the store (which are disjunct) as Treemaps, e.g. Tracks, Assets, Artists and dividing them into the different actions possible. The connection between the Treemaps (the actual netting) is then the amount of users which are equal within the parts. The edge is outlined with the smaller number of user giving the idea of ''the whole''.
# '''Parallel Coordinates:''' The Variables selected are normalized (to a range of 0-1) and plotted into a parallel coordinate plot. Techniques as Focus/Context must be appield to be able to figure out patterns.
# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Bubble Chart====
# The size of the bubble represents the number of actions taken within the part of the portal. Since the portal is semantically divided in parts in which you just can set actions on disjunct products, the different product types are also represented through this basic view.
# Different color segments of the bubble represent the (selected) type of actions.
# The position on the timeline represents all the actions taken until a special point of time. Moreover this can be regulated to show all actions in a special timespan.
# The "whole" is always visible through a very transparent bubblechart giving the idea of all the actions.
# The details to the segments and bubbles are becoming visible as Tiptext via the mousepointer when rolling over the segment or bubble.

====TreeNet====
# The size of the single Treemaps is the number of user activities in a certain part of the portal.
# The dividing is done by the selected user activities.
# The edges between the single maps are representing the users which are equal in both maps. By that is is possible to see how much activity is done between two parts of the portal.
# This edges can also be divided with respect to certain variables (e.g. profiled and unprofiled user).
# Zooming into depth of one treemap or edge is provided, shrinking the context and enlarging the desired section.

====Parallel Coordinates====

The following attributes should be a fix part of the chart:
# Product Type
# User Action
# maybe: Time from 0-24

The following attributes can additionally be added to the chart:
# Cluster
# Age of User
# Asset Type
# ...

Very important is that some attributes exclude themselves (like assets and tracks are disjunct groups). This is not a ''structure'' but defined.

It must be possible to add color for certain constraints to the data:
# The profiled and unprofiled user,
# the timespan of the actions,
# ...

Additionally:
# Brushing
# Linking

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# When selecting more than 3 variables a subset out of htese variables is used to construct the scatterplot. An iteration takes place over time and exchanges variables continously to show all the selected variables.
# To make the graphic more readable the user can add color with respect to certain variables.

====Morphing====
# Specify start and end point.
# Hit play to let morphing happen within the assigned timespan.
# Hit STOP to set the header to the starting time.
# Hit PAUSE to stop the header at any time.
# Drag and Drop the head to jump to a specific time.
# Switch between an absolute and relative time-axis.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (Focus -Context)
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When Making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
* 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
* 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
* 22.11.2005 - placement of first version of MICE tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching talk:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 2

2005-11-25T22:53:00Z

UE-InfoVis0506 0306110:

==Evolution==

=== General Developments (similar for all) ===

* Changing the intervalls of the scale to a equidistant one with 5 classes.
* Taking a (more or less) calm background for the graphic ba using a map without any saturation (referring to Data Ink Ration).
* Setting the background on a lower saturation to keep the focus on the symbols and the text naming the spots.
* Frameing the legend to give a clear idea about its position and size.
* Providing an (obligatory) measurement of the size of the land (lower right corner) which is necessary.
* By taking an additional color we wanted to rise the aestethic aspect of the graphic. Additionally this helps viewers to distinguish between symbols and text.

* Unterschiedlich grosse Mengen können durch verschieden grosse Symbole sehr gut (meiner Meinung nach am besten) dargestellt werden. Diese 'Kodierung' ist wirklich intuitiv zu verstehen (egal welches Symbol verwendet wird).
* Farben sind sinnvoll wenn sie notwendig sind, d.h. wenn dadurch ''zusätzliche'' Information gegeben wird. Ansonsten machen sie die Graphik "unruhig".
* 'Tortendiagramme' zeigen 'Anteile'. Sie suggerieren z.B. in diesem Beispiel das 500 inches 100 Prozent sind, und das sich alle anderen daran zu messen haben.
* Ich kann noch immer nicht sehen das Schwarz viel, und Rot wenig Schnee ist, und Hellrot noch weniger Schnee.
* Helle Flächen treten vor dunklen in den Vordergrund und umgekehrt (betrifft die Einfärbung des Hintergrunds).
* Eine Überschrift in einer anderen Farbe und Schrift macht die Graphik unnötig unruhig. Durch die gleiche (wenn auch größere) Schriftart der Überschrift wird die Zugehörigkeit zur Graphik betont (wenn man bedenkt, dass die Graphik eingebettet in andere, teilweise graphische, Elemente sein wird). Prinzip der Ähnlichkeit.

=== GRAFIK 1 ===
{|
|[[Image:SnowfallIdaho.jpg|100px|width=100px Idaho Snowfall - Snowflake 1]]
|align="left" valign="top"|Our first approach was to identify the users. We assumed the graphic to come from a touristic background. Therefore we provided a map of the shape of the land beneath the spots. The symboly mapping the classes of snow where developed in quite an effort, according to consistency and the rules for representing classified data.
|}

=== GRAFIK 2 ===

{|
|[[Image:SnowfallIdaho6.jpg|100px|Idaho Snowfall - Blue Snowflake Substitute Legend]]
|align="left" valign="top"|The discussions about the snowflakes from above would not stop, so we made up for a different mapping. We thought that it might be useful to keep the symbol itself the same size for all classes, but map the rise in snow through saturation in color. Additionally we made up lines in the circle to stress the quantity is rising by more lines. These lines had the form of a star, which schould understraigt the snow measurement. There is also a similarity to the '''Sunflower Plot''' as known from Exploratory Data Analysis (used for scatterplot with discrete values).
|}

=== GRAFIK 3 ===

{|
|[[Image:Idaho_Snowfall.png|100px|Idaho Snowfall - Piechart Legend]]
|align="left" valign="top"|Even another approach to the legend, and thus the mapping of the spots to the classes was apartially filled circle. This can be seen as a '''Pie Chart'''. In addition the color saturation should represent the amount of snow as well.
|}

=== GRAFIK 4 ===
{|
|[[Image:Idaho04.gif|100px|Idaho Snowfall - Bar Legend]]
|align="left" valign="top"|After finding out the exact values (internet research), we also wanted to look at a barplot approach. This turned out to not give a good overview about the situation as a whole.
|}

----

==Empirical Study==

For the reason of finding a decision for taking one of the above placed graphics, we made a survey with not involved persons. All of them had a technic related background and therefor some knowledge and experience with reading graphics.

1. Grafik (s/w mit Symbolen) 
6

2. Grafik (blaue Symbole) 
3

3. Grafik (rote Symbole) 
3

----
Bitte um Erhöhung des Zählers um 1 bei einer Stimme für die jeweilige Grafik als "beste".

----

== Bewertung ==
'''Punkte NEU: 19''' (Begründung siehe unten)

'''Punkte: 16'''

'''Begründung:'''
*Die Analyse der Grafik ist OK (allerdings habt ihr keine Bezüge zu den Designprinzipien hergestellt)
*Eure Verbesserung ist gut gelungen 
Hier noch einige Kommentare:
**Verbesserung des Hintergrundes ist sehr gut gelungen
**Umrandung der Legende könnte entfallen (ist ausserdem sehr dominant, weil eine dicke schwarze Linie verwendet wurde)
**Hintergrundverlauf bei Legende ist meiner Meinung nach nicht nötig bzw. kaum sichtbar.
**Veränderung der Skala in eine lineare ist sehr gut
**Die Snowflake Symbole sollten komplett weggelassen werden. (Die Information ist ohnehin in der Größe des Kreises kodiert. Die Symbole lenken eher davon ab - ausserdem stören sie den Eindruck der Flächenverhältnismäßigkeiten.)
**Was mir allerdings noch besser gefallen hat, ist euer Ansatz mit Balkendiagrammen. Denn in der jetzigen Form ist der Vergleich zwischein einzelnen Orten eher schwierig, weil sie über die Grafik verteilt sind. Mein Vorschlag wäre evt. eine Kombination mit einer Balkengrafikanzeige, die diese Vergleiche wesentlich vereinfachen würde.

Ihr habt die Möglichkeit, eure Ausarbeitung bis 16.11.2005 zu überarbeiten, um die derzeitigen Unzulänglichkeiten auszubessern. Wird das gemacht, so werden die Punkte danach klarerweise aufgewertet.

-- [[User:Iwolf|Wolfgang Aigner]] 18:58, 5 November 2005 (CET)

'''Begründung NEU:'''
* Verbesserung ist super!
* Eine Kleinigkeit: Ihr habt zwar eine Numerierung/Reihung beim Balkendiagramm, aber die Nummern finden sich in der Grafik unten nicht wieder...
* Warum sind bei euch die Balkendiagramme von rechts nach links aufgetragen und nicht von links nach rechts (normale Leserichtung)?
* Hinweis: Es gäbe noch eine Möglichkeit die Balkendiagramme lesbarer zu machen: (von E. Tufte [Tufte, 1991])

[[Image:Tufte-bar chart-redesign.gif|none|thumb|300px|Bar chart redesign.|[Tufte, 1991]]]

-- [[User:Iwolf|Wolfgang Aigner]] 11:04, 25 November 2005 (CET)

'''Comments'''
* The proposed changes are now applied in the graphic. Its revision is 11. - Stefan Schnabl, 25.11.2005

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-25T22:38:51Z

UE-InfoVis0506 0306110: /* Event Log */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Bubble Chart:''' Representing the parts of the online platform through bubbles. The traffic (or actions of people equal in the bubbles) is represented by the edges between the bubbles. Moreover the color inside the bubbles gives the proportions of persons as part of a selected group. A part stays grey representing "the rest".
# '''TreeNet:''' Showing the parts of the store (which are disjunct) as Treemaps, e.g. Tracks, Assets, Artists and dividing them into the different actions possible. The connection between the Treemaps (the actual netting) is then the amount of users which are equal within the parts. The edge is outlined with the smaller number of user giving the idea of ''the whole''.
# '''Parallel Coordinates:''' The Variables selected are normalized (to a range of 0-1) and plotted into a parallel coordinate plot. Techniques as Focus/Context must be appield to be able to figure out patterns.
# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Bubble Chart====
# The size of the bubble represents the number of actions taken within the part of the portal. Since the portal is semantically divided in parts in which you just can set actions on disjunct products, the different product types are also represented through this basic view.
# Different color segments of the bubble represent the (selected) type of actions.
# The position on the timeline represents all the actions taken until a special point of time. Moreover this can be regulated to show all actions in a special timespan.
# The "whole" is always visible through a very transparent bubblechart giving the idea of all the actions.
# The details to the segments and bubbles are becoming visible as Tiptext via the mousepointer when rolling over the segment or bubble.

====TreeNet====
# The size of the single Treemaps is the number of user activities in a certain part of the portal.
# The dividing is done by the selected user activities.
# The edges between the single maps are representing the users which are equal in both maps. By that is is possible to see how much activity is done between two parts of the portal.
# This edges can also be divided with respect to certain variables (e.g. profiled and unprofiled user).
# Zooming into depth of one treemap or edge is provided, shrinking the context and enlarging the desired section.

====Parallel Coordinates====

The following attributes should be a fix part of the chart:
# Product Type
# User Action
# maybe: Time from 0-24

The following attributes can additionally be added to the chart:
# Cluster
# Age of User
# Asset Type
# ...

Very important is that some attributes exclude themselves (like assets and tracks are disjunct groups). This is not a ''structure'' but defined.

It must be possible to add color for certain constraints to the data:
# The profiled and unprofiled user,
# the timespan of the actions,
# ...

Additionally:
# Brushing
# Linking

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# There can be used a set of varaibles which is then exchanged continously using a subset of 3 variables out of the selected ones at a time.
# There can be color added to the plot giving additional information (see Parallel Coordinates).

====Morphing====
# Specify start and end point.
# Hit play to let morphing happen within the assigned timespan.
# Hit STOP to set the header to the starting time.
# Hit PAUSE to stop the header at any time.
# Drag and Drop the head to jump to a specific time.
# Switch between an absolute and relative time-axis.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (Focus -Context)
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When Making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
* 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
* 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
* 22.11.2005 - placement of first version of MICE tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-25T22:38:38Z

UE-InfoVis0506 0306110: /* Event Log */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Bubble Chart:''' Representing the parts of the online platform through bubbles. The traffic (or actions of people equal in the bubbles) is represented by the edges between the bubbles. Moreover the color inside the bubbles gives the proportions of persons as part of a selected group. A part stays grey representing "the rest".
# '''TreeNet:''' Showing the parts of the store (which are disjunct) as Treemaps, e.g. Tracks, Assets, Artists and dividing them into the different actions possible. The connection between the Treemaps (the actual netting) is then the amount of users which are equal within the parts. The edge is outlined with the smaller number of user giving the idea of ''the whole''.
# '''Parallel Coordinates:''' The Variables selected are normalized (to a range of 0-1) and plotted into a parallel coordinate plot. Techniques as Focus/Context must be appield to be able to figure out patterns.
# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Bubble Chart====
# The size of the bubble represents the number of actions taken within the part of the portal. Since the portal is semantically divided in parts in which you just can set actions on disjunct products, the different product types are also represented through this basic view.
# Different color segments of the bubble represent the (selected) type of actions.
# The position on the timeline represents all the actions taken until a special point of time. Moreover this can be regulated to show all actions in a special timespan.
# The "whole" is always visible through a very transparent bubblechart giving the idea of all the actions.
# The details to the segments and bubbles are becoming visible as Tiptext via the mousepointer when rolling over the segment or bubble.

====TreeNet====
# The size of the single Treemaps is the number of user activities in a certain part of the portal.
# The dividing is done by the selected user activities.
# The edges between the single maps are representing the users which are equal in both maps. By that is is possible to see how much activity is done between two parts of the portal.
# This edges can also be divided with respect to certain variables (e.g. profiled and unprofiled user).
# Zooming into depth of one treemap or edge is provided, shrinking the context and enlarging the desired section.

====Parallel Coordinates====

The following attributes should be a fix part of the chart:
# Product Type
# User Action
# maybe: Time from 0-24

The following attributes can additionally be added to the chart:
# Cluster
# Age of User
# Asset Type
# ...

Very important is that some attributes exclude themselves (like assets and tracks are disjunct groups). This is not a ''structure'' but defined.

It must be possible to add color for certain constraints to the data:
# The profiled and unprofiled user,
# the timespan of the actions,
# ...

Additionally:
# Brushing
# Linking

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# There can be used a set of varaibles which is then exchanged continously using a subset of 3 variables out of the selected ones at a time.
# There can be color added to the plot giving additional information (see Parallel Coordinates).

====Morphing====
# Specify start and end point.
# Hit play to let morphing happen within the assigned timespan.
# Hit STOP to set the header to the starting time.
# Hit PAUSE to stop the header at any time.
# Drag and Drop the head to jump to a specific time.
# Switch between an absolute and relative time-axis.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (Focus -Context)
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When Making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
* 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
* 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
* 22.11.2005 - placement of first version of MICS tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching talk:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-25T22:38:19Z

UE-InfoVis0506 0306110:

'''Kommentar zum Konzept:'''

*Der Entwurf und Designvorschlag sind schon recht detailiert - super!
*Was bedeutet das Attribut "homeCluster"?
*Ihr beschreibt 2 Benutzergruppen mit veschiedenen Anforderungen - es wäre vielleicht besser sich auf einen Typ zu konzentrieren.
*Referenzen bzw. Weblinks fehlen bei "Known Solutions"
*Viele Fehler im Text
*Das Mantra lautet: Overview First - Zoom and Filter (nicht Zoom and Focus)
*Die Beschreibung der TreeNet Visualisierung ist nicht klar beschrieben.
*Ihr verwendet mehrere verschiedene interaktive Techniken - sehr gut.
*Was aber evt. auch Sinn macht, ist eine gleichzeitige Darstellung von mehreren Techniken nebeneinander - um verschiedene Aspekte untersuchen zu können. (Bei eurem Proposal kann man jeweils nur eine Methode betrachten)

'''Korrekturen am Artikel und Statements zur Beurteilung'''
*Gone through writing, corrected mantra- Stefan Schnabl, 25.11.2005
*The attribute "homeClusters" is alwas NULL in the table "album", hence has no active meaning for our usage. - Stefan Schnabl, 25.11.2005
*Both groups can gain knowledge out of the visualisation applied. E.g. owners see where is a lot of traffic, developers as well. This information is usefull for both. They can as well see the relative amout of profiled users vs. unprofiled ones. This can lead to interesting hypothesis for developers as well as for owners. (e.g. owners might think that the profiled users are more active, and by that send them mor special hints and newsletters, on the other hand, if for instance the amount of unprofiled users is very low it can indicate the need for a more useful entry mask for getting a profiled user). - Stefan Schnabl, 25.11.2005
*With all due respect but I dont understand what is unclear about the TreeNet technique. - Stefan Schnabl, 25.11.2005

-- [[User:Iwolf|Wolfgang Aigner]] 14:32, 25 November 2005 (CET)

Teaching talk:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-25T22:37:11Z

UE-InfoVis0506 0306110:

Teaching talk:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-25T22:34:43Z

UE-InfoVis0506 0306110:

Teaching talk:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-25T22:31:49Z

UE-InfoVis0506 0306110:

Teaching talk:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-25T22:31:32Z

UE-InfoVis0506 0306110:

Teaching talk:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-25T22:31:24Z

UE-InfoVis0506 0306110:

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-25T22:29:25Z

UE-InfoVis0506 0306110: /* Parallel Coordinates */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Bubble Chart:''' Representing the parts of the online platform through bubbles. The traffic (or actions of people equal in the bubbles) is represented by the edges between the bubbles. Moreover the color inside the bubbles gives the proportions of persons as part of a selected group. A part stays grey representing "the rest".
# '''TreeNet:''' Showing the parts of the store (which are disjunct) as Treemaps, e.g. Tracks, Assets, Artists and dividing them into the different actions possible. The connection between the Treemaps (the actual netting) is then the amount of users which are equal within the parts. The edge is outlined with the smaller number of user giving the idea of ''the whole''.
# '''Parallel Coordinates:''' The Variables selected are normalized (to a range of 0-1) and plotted into a parallel coordinate plot. Techniques as Focus/Context must be appield to be able to figure out patterns.
# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Bubble Chart====
# The size of the bubble represents the number of actions taken within the part of the portal. Since the portal is semantically divided in parts in which you just can set actions on disjunct products, the different product types are also represented through this basic view.
# Different color segments of the bubble represent the (selected) type of actions.
# The position on the timeline represents all the actions taken until a special point of time. Moreover this can be regulated to show all actions in a special timespan.
# The "whole" is always visible through a very transparent bubblechart giving the idea of all the actions.
# The details to the segments and bubbles are becoming visible as Tiptext via the mousepointer when rolling over the segment or bubble.

====TreeNet====
# The size of the single Treemaps is the number of user activities in a certain part of the portal.
# The dividing is done by the selected user activities.
# The edges between the single maps are representing the users which are equal in both maps. By that is is possible to see how much activity is done between two parts of the portal.
# This edges can also be divided with respect to certain variables (e.g. profiled and unprofiled user).
# Zooming into depth of one treemap or edge is provided, shrinking the context and enlarging the desired section.

====Parallel Coordinates====

The following attributes should be a fix part of the chart:
# Product Type
# User Action
# maybe: Time from 0-24

The following attributes can additionally be added to the chart:
# Cluster
# Age of User
# Asset Type
# ...

Very important is that some attributes exclude themselves (like assets and tracks are disjunct groups). This is not a ''structure'' but defined.

It must be possible to add color for certain constraints to the data:
# The profiled and unprofiled user,
# the timespan of the actions,
# ...

Additionally:
# Brushing
# Linking

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# There can be used a set of varaibles which is then exchanged continously using a subset of 3 variables out of the selected ones at a time.
# There can be color added to the plot giving additional information (see Parallel Coordinates).

====Morphing====
# Specify start and end point.
# Hit play to let morphing happen within the assigned timespan.
# Hit STOP to set the header to the starting time.
# Hit PAUSE to stop the header at any time.
# Drag and Drop the head to jump to a specific time.
# Switch between an absolute and relative time-axis.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (Focus -Context)
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When Making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
# 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
# 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
# 22.11.2005 - placement of first version of MICS tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-25T22:25:59Z

UE-InfoVis0506 0306110: /* TreeNet */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Bubble Chart:''' Representing the parts of the online platform through bubbles. The traffic (or actions of people equal in the bubbles) is represented by the edges between the bubbles. Moreover the color inside the bubbles gives the proportions of persons as part of a selected group. A part stays grey representing "the rest".
# '''TreeNet:''' Showing the parts of the store (which are disjunct) as Treemaps, e.g. Tracks, Assets, Artists and dividing them into the different actions possible. The connection between the Treemaps (the actual netting) is then the amount of users which are equal within the parts. The edge is outlined with the smaller number of user giving the idea of ''the whole''.
# '''Parallel Coordinates:''' The Variables selected are normalized (to a range of 0-1) and plotted into a parallel coordinate plot. Techniques as Focus/Context must be appield to be able to figure out patterns.
# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Bubble Chart====
# The size of the bubble represents the number of actions taken within the part of the portal. Since the portal is semantically divided in parts in which you just can set actions on disjunct products, the different product types are also represented through this basic view.
# Different color segments of the bubble represent the (selected) type of actions.
# The position on the timeline represents all the actions taken until a special point of time. Moreover this can be regulated to show all actions in a special timespan.
# The "whole" is always visible through a very transparent bubblechart giving the idea of all the actions.
# The details to the segments and bubbles are becoming visible as Tiptext via the mousepointer when rolling over the segment or bubble.

====TreeNet====
# The size of the single Treemaps is the number of user activities in a certain part of the portal.
# The dividing is done by the selected user activities.
# The edges between the single maps are representing the users which are equal in both maps. By that is is possible to see how much activity is done between two parts of the portal.
# This edges can also be divided with respect to certain variables (e.g. profiled and unprofiled user).
# Zooming into depth of one treemap or edge is provided, shrinking the context and enlarging the desired section.

====Parallel Coordinates====

The following attributes should be a fix part of the chart:
# Product Type
# User Action
# maybe: Time from 0-24

The following attributes can be additionally added to the chart:
# Cluster
# Age of User
# Asset Type
# ...

Very important is that some attributes exclude themselves (like assets and tracks are disjunct groups). This is not a ''structure'' but defined.

It must be possible to add color for certain constraints to the data:
# The profiled and unprofiled user
# the timespan of the actions
# ...

Additionally:
# Brushing
# Linking

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# There can be used a set of varaibles which is then exchanged continously using a subset of 3 variables out of the selected ones at a time.
# There can be color added to the plot giving additional information (see Parallel Coordinates).

====Morphing====
# Specify start and end point.
# Hit play to let morphing happen within the assigned timespan.
# Hit STOP to set the header to the starting time.
# Hit PAUSE to stop the header at any time.
# Drag and Drop the head to jump to a specific time.
# Switch between an absolute and relative time-axis.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (Focus -Context)
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When Making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
# 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
# 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
# 22.11.2005 - placement of first version of MICS tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-25T22:04:17Z

UE-InfoVis0506 0306110: /* Problems and Tasks to Solve */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Filter - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Bubble Chart:''' Representing the parts of the online platform through bubbles. The traffic (or actions of people equal in the bubbles) is represented by the edges between the bubbles. Moreover the color inside the bubbles gives the proportions of persons as part of a selected group. A part stays grey representing "the rest".
# '''TreeNet:''' Showing the parts of the store (which are disjunct) as Treemaps, e.g. Tracks, Assets, Artists and dividing them into the different actions possible. The connection between the Treemaps (the actual netting) is then the amount of users which are equal within the parts. The edge is outlined with the smaller number of user giving the idea of ''the whole''.
# '''Parallel Coordinates:''' The Variables selected are normalized (to a range of 0-1) and plotted into a parallel coordinate plot. Techniques as Focus/Context must be appield to be able to figure out patterns.
# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Bubble Chart====
# The size of the bubble represents the number of actions taken within the part of the portal. Since the portal is semantically divided in parts in which you just can set actions on disjunct products, the different product types are also represented through this basic view.
# Different color segments of the bubble represent the (selected) type of actions.
# The position on the timeline represents all the actions taken until a special point of time. Moreover this can be regulated to show all actions in a special timespan.
# The "whole" is always visible through a very transparent bubblechart giving the idea of all the actions.
# The details to the segments and bubbles are becoming visible as Tiptext via the mousepointer when rolling over the segment or bubble.

====TreeNet====
# The size of the single Treemaps is the number of user activities.
# The Dividing is done by the selected user activities.
# The edges between the single maps is representing the users which are equal in both maps.
# This edges can also be divided with respect to certain variables (e.g. profiled and unprofiled user).
# Zooming into septh of one treemap or edge is provided, shrinking the context and enlarging the desired section.

====Parallel Coordinates====

The following attributes should be a fix part of the chart:
# Product Type
# User Action
# maybe: Time from 0-24

The following attributes can be additionally added to the chart:
# Cluster
# Age of User
# Asset Type
# ...

Very important is that some attributes exclude themselves (like assets and tracks are disjunct groups). This is not a ''structure'' but defined.

It must be possible to add color for certain constraints to the data:
# The profiled and unprofiled user
# the timespan of the actions
# ...

Additionally:
# Brushing
# Linking

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# There can be used a set of varaibles which is then exchanged continously using a subset of 3 variables out of the selected ones at a time.
# There can be color added to the plot giving additional information (see Parallel Coordinates).

====Morphing====
# Specify start and end point.
# Hit play to let morphing happen within the assigned timespan.
# Hit STOP to set the header to the starting time.
# Hit PAUSE to stop the header at any time.
# Drag and Drop the head to jump to a specific time.
# Switch between an absolute and relative time-axis.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (Focus -Context)
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When Making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
# 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
# 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
# 22.11.2005 - placement of first version of MICS tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-25T22:04:06Z

UE-InfoVis0506 0306110: /* Goals and Objectives */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlations between shopping habbits and other activities of the users.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Focus - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Bubble Chart:''' Representing the parts of the online platform through bubbles. The traffic (or actions of people equal in the bubbles) is represented by the edges between the bubbles. Moreover the color inside the bubbles gives the proportions of persons as part of a selected group. A part stays grey representing "the rest".
# '''TreeNet:''' Showing the parts of the store (which are disjunct) as Treemaps, e.g. Tracks, Assets, Artists and dividing them into the different actions possible. The connection between the Treemaps (the actual netting) is then the amount of users which are equal within the parts. The edge is outlined with the smaller number of user giving the idea of ''the whole''.
# '''Parallel Coordinates:''' The Variables selected are normalized (to a range of 0-1) and plotted into a parallel coordinate plot. Techniques as Focus/Context must be appield to be able to figure out patterns.
# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Bubble Chart====
# The size of the bubble represents the number of actions taken within the part of the portal. Since the portal is semantically divided in parts in which you just can set actions on disjunct products, the different product types are also represented through this basic view.
# Different color segments of the bubble represent the (selected) type of actions.
# The position on the timeline represents all the actions taken until a special point of time. Moreover this can be regulated to show all actions in a special timespan.
# The "whole" is always visible through a very transparent bubblechart giving the idea of all the actions.
# The details to the segments and bubbles are becoming visible as Tiptext via the mousepointer when rolling over the segment or bubble.

====TreeNet====
# The size of the single Treemaps is the number of user activities.
# The Dividing is done by the selected user activities.
# The edges between the single maps is representing the users which are equal in both maps.
# This edges can also be divided with respect to certain variables (e.g. profiled and unprofiled user).
# Zooming into septh of one treemap or edge is provided, shrinking the context and enlarging the desired section.

====Parallel Coordinates====

The following attributes should be a fix part of the chart:
# Product Type
# User Action
# maybe: Time from 0-24

The following attributes can be additionally added to the chart:
# Cluster
# Age of User
# Asset Type
# ...

Very important is that some attributes exclude themselves (like assets and tracks are disjunct groups). This is not a ''structure'' but defined.

It must be possible to add color for certain constraints to the data:
# The profiled and unprofiled user
# the timespan of the actions
# ...

Additionally:
# Brushing
# Linking

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# There can be used a set of varaibles which is then exchanged continously using a subset of 3 variables out of the selected ones at a time.
# There can be color added to the plot giving additional information (see Parallel Coordinates).

====Morphing====
# Specify start and end point.
# Hit play to let morphing happen within the assigned timespan.
# Hit STOP to set the header to the starting time.
# Hit PAUSE to stop the header at any time.
# Drag and Drop the head to jump to a specific time.
# Switch between an absolute and relative time-axis.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (Focus -Context)
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When Making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
# 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
# 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
# 22.11.2005 - placement of first version of MICS tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-25T22:03:50Z

UE-InfoVis0506 0306110: /* Known Solutions / Methods (related to the target group) */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

As a matter of fact these methods are all very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlation between shopping habbits and other activities.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Focus - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Bubble Chart:''' Representing the parts of the online platform through bubbles. The traffic (or actions of people equal in the bubbles) is represented by the edges between the bubbles. Moreover the color inside the bubbles gives the proportions of persons as part of a selected group. A part stays grey representing "the rest".
# '''TreeNet:''' Showing the parts of the store (which are disjunct) as Treemaps, e.g. Tracks, Assets, Artists and dividing them into the different actions possible. The connection between the Treemaps (the actual netting) is then the amount of users which are equal within the parts. The edge is outlined with the smaller number of user giving the idea of ''the whole''.
# '''Parallel Coordinates:''' The Variables selected are normalized (to a range of 0-1) and plotted into a parallel coordinate plot. Techniques as Focus/Context must be appield to be able to figure out patterns.
# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Bubble Chart====
# The size of the bubble represents the number of actions taken within the part of the portal. Since the portal is semantically divided in parts in which you just can set actions on disjunct products, the different product types are also represented through this basic view.
# Different color segments of the bubble represent the (selected) type of actions.
# The position on the timeline represents all the actions taken until a special point of time. Moreover this can be regulated to show all actions in a special timespan.
# The "whole" is always visible through a very transparent bubblechart giving the idea of all the actions.
# The details to the segments and bubbles are becoming visible as Tiptext via the mousepointer when rolling over the segment or bubble.

====TreeNet====
# The size of the single Treemaps is the number of user activities.
# The Dividing is done by the selected user activities.
# The edges between the single maps is representing the users which are equal in both maps.
# This edges can also be divided with respect to certain variables (e.g. profiled and unprofiled user).
# Zooming into septh of one treemap or edge is provided, shrinking the context and enlarging the desired section.

====Parallel Coordinates====

The following attributes should be a fix part of the chart:
# Product Type
# User Action
# maybe: Time from 0-24

The following attributes can be additionally added to the chart:
# Cluster
# Age of User
# Asset Type
# ...

Very important is that some attributes exclude themselves (like assets and tracks are disjunct groups). This is not a ''structure'' but defined.

It must be possible to add color for certain constraints to the data:
# The profiled and unprofiled user
# the timespan of the actions
# ...

Additionally:
# Brushing
# Linking

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# There can be used a set of varaibles which is then exchanged continously using a subset of 3 variables out of the selected ones at a time.
# There can be color added to the plot giving additional information (see Parallel Coordinates).

====Morphing====
# Specify start and end point.
# Hit play to let morphing happen within the assigned timespan.
# Hit STOP to set the header to the starting time.
# Hit PAUSE to stop the header at any time.
# Drag and Drop the head to jump to a specific time.
# Switch between an absolute and relative time-axis.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (Focus -Context)
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When Making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
# 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
# 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
# 22.11.2005 - placement of first version of MICS tool

----
< [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8|G8]] 
<< [[Teaching:TUW_-_UE_InfoVis_WS_2005/06|overview]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3

2005-11-25T22:03:38Z

UE-InfoVis0506 0306110: /* Known Solutions / Methods (related to the target group) */

==Topic==
On-line Music Portals: Analyzing the Users' Activities

==Area of Application==
This section describes the application area of 
'''MICE - Music Investigation and Clustering Environment'''.

[[Image:MICELogo.gif]] [Stefan Schnabl, 2005]

===Analysis of Application Area===

====General Description====
The dataset contains data of a special online music portal. 
http://www.ericsson-mediasuite.com/music/web2/dyn/home

The great aim is to get information out about the activity of the users in the special areas. Maybe this can help to find out some hypothesis how the portal can be optimzies or restructured. 
If a siginificant correlation between certain aspects can be found, this could be interesting for the portal owners and the technicians how established the portal.

====Special Issues====
We identified the following spots of interest:
# The user of MICE has different intentions than the person the data is about
# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge. Hence it is not easy to derive information from the results of these techniques.
# The questions which are to be solved will gain complexity once they are answered (report chain).
# The answering will not be a single "result" but a (iterative) process.
# Heavy task to realize an abstract graphical view on the data due to multidimensionality.

===Analysis of the Dataset===
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
{|
|width="130"| - Album||Represents all albums available.
|-
| - Artist||Contains information about the known artists.
|-
| - Track||References between ''Album'' and ''Artist''.
|-
| - Asset||Information concerning the products which are sold.
|-
| - Playlist||Contains information about the playlists created by the users of the on-line portal.
|-
| - Playlist Item ||Associative reference between ''Track'' and ''Playlist''.
|-
| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist''.
|-
| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist''.
|}
The user behaviour results in the tables ''User'' and ''User action''. All the other information is brought in by companies distributing such content.
====Description of the Datatypes====
The tables consist of the following Data items:
=====Album=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|title||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|albumId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Artist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|defaultAssetId||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|firstName||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|artistId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|lastName||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|homeClusters||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|artistId||Primary Key|| ||
|}
=====Track=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|trackId||Integer (20 Bytes) [key]||0||not NULL
|-
|bgcolor="#eeeeee"|title||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|ISRC||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|homeCluster||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|albumId||String (255 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|trackId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Asset=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|artistId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetType||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|creationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|not NULL
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|assetId||bgcolor="#eeeeee"|Primary Key ||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|claim||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|creationDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|playlistId||Integer (20 Bytes)||0||not NULL
|-
|bgcolor="#eeeeee"|playListName||bgcolor="#eeeeee"|String (255 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|userId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}
=====Playlist Item=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|trackId||Integer (20 Bytes) [key]||NULL||
|-
|bgcolor="#eeeeee"|playlistId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|}
=====User=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|handSetId||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes)||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|NOT NULL, auto increment
|-
|language||String (255 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|lastLoginDate||bgcolor="#eeeeee"|DateTime||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|registrationDate||DateTime||NULL||
|-
|bgcolor="#eeeeee"|gender||bgcolor="#eeeeee"|Character||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|yearOfBirth||unsigned Integer (10 Bytes)||NULL||
|-
|bgcolor="#eeeeee"|genrePref||bgcolor="#eeeeee"|String(250 Bytes)||bgcolor="#eeeeee"|NULL||bgcolor="#eeeeee"|
|-
|favouredArtists||String (no size specified)||
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Primary Key||bgcolor="#eeeeee"| ||bgcolor="#eeeeee"|
|}

=====User Action=====
{|
|bgcolor="#e0e0e0" width="130"|'''Field Name'''||width="130" bgcolor="#e0e0e0"|'''Datatype'''||width="130" bgcolor="#e0e0e0"|'''Default value'''||width="130" bgcolor="#e0e0e0"|'''Constraints'''
|-
|context||Integer (11 Bytes)||-1||NOT NULL
|-
|bgcolor="#eeeeee"|userId||bgcolor="#eeeeee"|Integer (20 Bytes) [key]||bgcolor="#eeeeee"|0||bgcolor="#eeeeee"|NOT NULL
|-
|productId||Integer (20 Bytes) [key]||0||NOT NULL
|-
|bgcolor="#eeeeee"|actionTime||bgcolor="#eeeeee"|TimeStamp [key]||bgcolor="#eeeeee"|0000-00-00 00:00:00||bgcolor="#eeeeee"|NOT NULL
|-
|action||String (20 Bytes)||<emtpy>||NOT NULL
|-
|bgcolor="#eeeeee"|productType||bgcolor="#eeeeee"|String (20 Bytes)||bgcolor="#eeeeee"|<emtpy>||bgcolor="#eeeeee"|NOT NULL
|}

====Description of the Datastructures====
The tables are in touch with each other in the following way: The ''artist'' entity can be considered as the central point of the structure. It may be referenced to none or more assets, one or more ''album'', one or more ''tracks'' and none or several ''user actions''. ''Tracks'' and ''assets'' can also be referenced to several ''user actions''. Due to the circumstance that a ''user action'' is always being executed by a user, there has to be a ''user'' - entitiy which can be referenced to several ''user actions''. Further, ''playlists'' can be created by a ''user'' which results in a 1:n reference between ''user'' and the ''playlist''-entity. The reference between the playlist and the contained tracks which is used to be a m:n reference is split up by the ''playlist item''-entity.
Finally ''tracks'' can be referenced to one or more ''clusters'' and a ''cluster'' may be in relation to 'super clusters', which are also contained in the ''cluster'' entity.

[[Image:MiceERD.png]]

The symbols have to be interpreted in the following way: [[Image:MiceRelation.png]] represents a 1:n reference between the entities A and B. One item of the entity B is referred to several items of the entity A. Further the minimum numbers of references may be specified with a small circle or a short line which can be interpreted as 0 or 1. So a circle (0) means that the reference pointer may be set to NULL, whereas a short line (1) means that there has to be at least one reference.

==Target Group==

===Identifying the Target===
The target for the exploration tool are mainly '''shopping analysts''' and '''web developer'''. 
It is the aim to suit the needs of persons who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, and by that help the developers of such systems.

===Special Issues of the Target Group===
# Shop Owners: Extremely '''business focused'''.
# Web Developers: Extremely '''technically focused'''.
# Maybe forgetting the users needs to "feel good".

===Known Solutions / Methods (related to the target group)===
# (statisitcal) Data Mining mthods (e.g. Shoppingcart analysis, Principal Components Analysis, Cluster Analysis)
# SASS Enterprise Miner
# SPSS Statistical Investigation Tool

=> All very heavily statistically oriented.

==Intended Purpose==

===Goals and Objectives===
# Find correlation between shopping habbits and other activities.
# Find evidence for user activity with respect to profiled and unprofiled user.
# Linking between different visualization techniques.
# Zoom and Filter into more detail.

===Problems and Tasks to Solve===
# Giving a good overview without cutting away to much paths to the depth of the data.
# Clear mapping of variables.
# Clarify wht the user can expect by using certain buttons and methods.
# Cear structure of the methods.
# Consistency in usage of buttons and colors.
# Good guidance about wht to do with the results of a step of a method.
# Keep focused on matra: Overview First - Zoom and Focus - Details on Demand!

===Example Questions===
# In which timespan was a lot of activity?
# What are '''the buyers''' doing before or after they bought?
# How many people are visiting the different parts of the portal?
# Is VIEW and BUY correlated?
# Are there '''impulse buyers'''?

==Proposed Design==

===Types of Visualization Applied===
# '''Bubble Chart:''' Representing the parts of the online platform through bubbles. The traffic (or actions of people equal in the bubbles) is represented by the edges between the bubbles. Moreover the color inside the bubbles gives the proportions of persons as part of a selected group. A part stays grey representing "the rest".
# '''TreeNet:''' Showing the parts of the store (which are disjunct) as Treemaps, e.g. Tracks, Assets, Artists and dividing them into the different actions possible. The connection between the Treemaps (the actual netting) is then the amount of users which are equal within the parts. The edge is outlined with the smaller number of user giving the idea of ''the whole''.
# '''Parallel Coordinates:''' The Variables selected are normalized (to a range of 0-1) and plotted into a parallel coordinate plot. Techniques as Focus/Context must be appield to be able to figure out patterns.
# '''(multidimensional) Scatterplot:''' A Scatterplot helping to understand (maybe in more detail) the relations of some variables.
# '''Morphing (timeline):''' The datasets are timestamped and thus can ve used to make a morphing of the plots. By that it is possible to see whether there are special occurences (maybe heavy advertisement leads to more activity).

===Visual Mapping===
The basic data represented are '''user actions'''. The essential table should be enhanced with a column indicating the user as an '''profiled''' user (or not).

====Bubble Chart====
# The size of the bubble represents the number of actions taken within the part of the portal. Since the portal is semantically divided in parts in which you just can set actions on disjunct products, the different product types are also represented through this basic view.
# Different color segments of the bubble represent the (selected) type of actions.
# The position on the timeline represents all the actions taken until a special point of time. Moreover this can be regulated to show all actions in a special timespan.
# The "whole" is always visible through a very transparent bubblechart giving the idea of all the actions.
# The details to the segments and bubbles are becoming visible as Tiptext via the mousepointer when rolling over the segment or bubble.

====TreeNet====
# The size of the single Treemaps is the number of user activities.
# The Dividing is done by the selected user activities.
# The edges between the single maps is representing the users which are equal in both maps.
# This edges can also be divided with respect to certain variables (e.g. profiled and unprofiled user).
# Zooming into septh of one treemap or edge is provided, shrinking the context and enlarging the desired section.

====Parallel Coordinates====

The following attributes should be a fix part of the chart:
# Product Type
# User Action
# maybe: Time from 0-24

The following attributes can be additionally added to the chart:
# Cluster
# Age of User
# Asset Type
# ...

Very important is that some attributes exclude themselves (like assets and tracks are disjunct groups). This is not a ''structure'' but defined.

It must be possible to add color for certain constraints to the data:
# The profiled and unprofiled user
# the timespan of the actions
# ...

Additionally:
# Brushing
# Linking

====(multidimensional) Scatterplot====
# All Varaibles can be assigned to an axis.
# There is a maximum of 3 variables at the same time.
# There can be used a set of varaibles which is then exchanged continously using a subset of 3 variables out of the selected ones at a time.
# There can be color added to the plot giving additional information (see Parallel Coordinates).

====Morphing====
# Specify start and end point.
# Hit play to let morphing happen within the assigned timespan.
# Hit STOP to set the header to the starting time.
# Hit PAUSE to stop the header at any time.
# Drag and Drop the head to jump to a specific time.
# Switch between an absolute and relative time-axis.

===Description of Used Techniques===
(see above)

===Possibilities of Interaction===
# In any stage of the charts one can make use of the '''timeline''' to focus (Focus -Context)
# Hook on on/off checkboxes at the left to include/exclude certain user actions (Add / Remove Detail).
# Assign color to charts to represent special groups (e.g. profiled and unprofiled user)
# Assign different symbols (where possible) instead of colors.
# Rotation of subsamples of variables when selecting more than 3 (Scatterplot).
# Zoom to special Bubble down to the one user action (Zooming).
# Providing user data for the one user on demand and showing data in detail section (Details on Demand).
# When switching between the methods apply selection automatically to new method (Linking).
# When Making a selection of a special timespan, apply it in all method screens (Linking).
# Select different lines out of the Parallel Coordinate Plot and keep selection visible in the other plots (Brushing).

===Mockups / Fake Screenshots===
[[image:Screenshot2_structure.jpg]]
 
 
[[image:Screenshot2_small.jpg]]

===Prototype===

http://www.sunarts.at/Studium/InfoVis/MICE.html

====Event Log====
# 25.11.2005 - introducing the playhead with speed controll and PLAY, PAUSE and STOP buttons
# 23.11.2005 - introducing the timeline with the dynamic left and right boundaries
# 22.11.2005 - placement of first version of MICS tool

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