Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G8 - Aufgabe 3: Difference between revisions
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| - Artist||Contains information about the known artists | | - Artist||Contains information about the known artists | ||
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| - Track|| | | - Track||References between ''Album'' and ''Artist'' | ||
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| - Asset|| | | - Asset||Information concerning the products which are sold. | ||
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| - Playlist||Contains information about the playlists created by the users of the on-line portal | | - Playlist||Contains information about the playlists created by the users of the on-line portal | ||
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| - User||Information concerning the users of the on-line portal. Referenced to ''Playlist'' | | - User||Information concerning the users of the on-line portal. Referenced to ''Playlist'' | ||
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| - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' | | - User action||Data containing information about the operation a user executes within the on-line portal. Referenced to ''User'' and ''Track'', ''Asset'', or ''Artist'' | ||
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Revision as of 19:34, 20 November 2005
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.
Analysis of Application Area
General Description
Shopping analysis, Cluster / Pattern analysis, Web based area of application, all data analysed comes form online users, areas of application include shopping optimazation, web application development, technical optimization. The tool might also be interesting for pure online stores.
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
- The questions wanted to be solved will gain on complexity once they are answered (report chain)
- The answering will not be a single "result" but a (iterative) process.
- Heavy task to realize a graphical abstryact view on the data.
- ...
Analysis of the Dataset
The data is organized in several tables which refer to each other by Item Ids. The following tables are available:
- 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 |
Description of the Datatypes
The tables consist of the following Data items:
Album
Field Name | Datatype | Default value | Constraints |
artistId | String (255 Bytes) [key] | NULL | |
homeClusters | String (255 Bytes) | NULL | |
creationDate | DateTime | NULL | |
albumId | Integer (20 Bytes) | 0 | not NULL |
title | String (255 Bytes) [key] | NULL | |
albumId | Primary Key |
Artist
Field Name | Datatype | Default value | Constraints |
creationDate | DateTime | NULL | |
defaultAssetId | String (255 Bytes) | NULL | |
firstName | String (255 Bytes) | NULL | |
artistId | Integer (20 Bytes) | 0 | not NULL |
lastName | String (255 Bytes) [key] | NULL | |
homeClusters | String (255 Bytes) | NULL | |
artistId | Primary Key |
Track
Field Name | Datatype | Default value | Constraints |
artistId | Integer (20 Bytes) | NULL | |
creationDate | DateTime [key] | NULL | |
trackId | Integer (20 Bytes) [key] | 0 | not NULL |
title | String (255 Bytes) | NULL | |
ISRC | String (255 Bytes) | NULL | |
homeCluster | String (255 Bytes) | NULL | |
albumId | String (255 Bytes) [key] | NULL | |
trackId | Primary Key |
Asset
Field Name | Datatype | Default value | Constraints |
artistId | Integer (20 Bytes) [key] | NULL | |
assetType | String (255 Bytes) | NULL | |
creationDate | DateTime | NULL | |
assetId | Integer (20 Bytes) | 0 | not NULL |
trackId | Integer (20 Bytes) [key] | NULL | |
assetId | Primary Key |
Playlist
Field Name | Datatype | Default value | Constraints |
claim | String (255 Bytes) | NULL | |
creationDate | DateTime | NULL | |
playlistId | Integer (20 Bytes) | 0 | not NULL |
playListName | String (255 Bytes) | NULL | |
userId | Integer (20 Bytes) [key] | NULL | |
playlistId | Primary Key |
Playlist Item
Field Name | Datatype | Default value | Constraints |
trackId | Integer (20 Bytes) [key] | NULL | |
playlistId | Integer (20 Bytes) [key] | NULL |
User
Field Name | Datatype | Default value | Constraints |
handSetId | String (255 Bytes) | NULL | |
userId | Integer (20 Bytes) | NOT NULL, auto increment | |
language | String (255 Bytes) | NULL | |
lastLoginDate | DateTime | NULL | |
registrationDate | DateTime | NULL | |
gender | Character | NULL | |
yearOfBirth | unsigned Integer (10 Bytes) | NULL | |
genrePref | String(250 Bytes) | NULL | |
favouredArtists | String | ||
userId | Primary Key |
User Action
Field Name | Datatype | Default value | Constraints |
context | Integer (11 Bytes) | -1 | NOT NULL |
userId | Integer (20 Bytes) [key] | 0 | NOT NULL |
productId | Integer (20 Bytes) [key] | 0 | NOT NULL |
actionTime | TimeStamp [key] | 0000-00-00 00:00:00 | NOT NULL |
action | String (20 Bytes) | <emtpy> | NOT NULL |
productType | String (20 Bytes) | <emtpy> | NOT NULL |
Description of the Datastructures
[description...]
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 persone who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, abd by that help the developers of such systems.
Special Issues of the Target Group
- Extremely business focused.
- Extremely technically focused.
- Maybe forgetting the users needs to "ffel good".
- ...
Intended Purpose
Goals and Objectives
Problems and Tasks to Solve
Example Questions
Proposed Design
Types of Visualization Applied
Visual Mapping
(Datadimension => Attribute)