InfoVis:Wiki - User contributions [en]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 3 - Design

2005-12-02T09:40:37Z

UE-InfoVis0506 0026030: /* Questions that should be solved with this visualization technique */

== Topic ==
'''MP3 Archive Visualization'''
== Specification of the Application Area and the given Dataset ==
=== Application area Analysis ===

The application area in this case is the creation of a clearly arranged visualization for a big music archive consisting of several thousand files. This can be achieved by using the values of the ID3-tags as well as the attributes of the music files itself.

Since the existing data mainly consist of discrete values, our mission to produce an appropriate visualization will basically be achieved with the creation of quantitative data by using a reasonable combination of given values.

Most Audio formats nowadays use the container format ID3 to store additional Information about the file, apart from the given file properties.

ID3 generally supports loads of different input values and to visualize them all would go beyond the scope of our task. Besides in most cases only the most common values like Album, Interpret, Year… are specified correctly.
Therefore we will concentrate our work for our prototype on these entries.

=== Dataset Analysis ===

{|
|- style="background:#FF6633"
!Attribute
!Data type
!Description
|-
|Title
|Discrete
|Name of the Song
|- style="background:#FFCC99"
|Interpret
|Discrete
|Name of the Artist/Band who plays the song
|-
|Album
|Discrete
|Name of the Album/Compilation the song belongs to
|- style="background:#FFCC99"
|Year
|Ordinal
|Year the song was recorded in
|-
|Genre
|Nominal
|Genre the song belongs to
|- style="background:#FFCC99"
|Size
|Quantitative
|Size of the file
|-
|Duration
|Quantitative
|Overall play length of the song
|- style="background:#FFCC99"
|Bit rate
|Ordinal
|Bit rate the file was encoded with
|-
|Sample rate
|Ordinal
|Sample rate the file was encoded with
|- style="background:#FFCC99"
|File Location
|Discrete
|Full system path to the file
|}

A complete data record consists of all attributes listed above.

All data in this set is one-dimensional.

== Analysis of the Target User Group ==

=== Who should use this kind of visualization technique? ===

This visualization technique is mainly meant for the 'end-users', that is someone who collects lots of MP3s. With 'lots of MP3s' we mean quite a few GBs, just more than 30 GBs. Our visualization should help the user to get an overview of his collection and his listening-habits.
This visualization technique could also be interesting for the band and the music industry, if they want to produce a new album. for example: a band (like radiohead) who changed their musicstyle over the years and wants to know which style is preferd more. But therefor they have to compare these dates from many users.

=== What are the characteristics of the target group? ===

People of this group are music enthusiasts. They have thousands of mp3s on their harddisk and love it to collect them. Most of them have lost track of their collection, on the strength of the abundance of their collection. These people mainly receive their mp3s from the internet instead of buying CDs, because they like to see their whole musiccollection at a glance.

=== Are there any known / often used Methods / Visualisation Techniques? ===

No, we don't know any similar visualization technique. ITunes only shows textbasedly how often a song was heard.

== Intended Purpose of our Visualization ==

=== What should be achieved with this visualization? ===
A better information representation of the MP3s should be achieved. Our visualization should help the user to get an overview of his collection and his listening-habits. He will get informations to the tracks, in reference to a special artist, which he often listens to and to those which he has never heard before. The representation of the data should be expressive, precise and self-explanatory.

=== Which tasks should be solved? ===
By using this visualization technique, the user will get informations to one chosen artist and whose discography. For example: in his database the user has got the band radiohead, who produced albums over 15 years and in this period they changed their musicstyle from alternative rock to experimental electronic. The visualization will show him from which producing period he has got more MP3s and which period he likes more, by counting the number of listenings of each song. The result could be that he has got more MP3s from their early years, but likes the experimental electronic tracks more.

=== Questions that should be solved with this visualization technique ===

== Proposal of Design ==

=== Types of Visualization applied ===

As there are no comparable features implemented in existing mp3 archival storage systems we decided to implement a visualization kit exploring the quantitative distribution of an mp3-archive according to the different genres. As a default the main window shows several genres the authors considered to be the right ones for music main-genres in the root directory including their percentage-rate corresponding to the whole file-archive.

By a single click on one of the main genres the user gets additional information in the upper right window on the one hand and of course a file-listing in the lower left window on the other hand. Double clicking a genre in the main window brings the user straight to a sub-level containing the corresponding sub-genres again shown in the main window. Through the path shown on top of the main window the system allows to easily jump back to wherever necessary out of every level.

Further on the user is able to read out file-specific information like Title, Interpret,
Album, Year, Duration & Bit-Rate through a click on the particular file listed below, but is also allowed to save a play-list from the files selected via the file-explorer.

Following feature turns the visualization kit into a rather complex and outstanding tool. By default the tool sorts the input data through a given hierarchy according to the main genres and alternatively to the sub-genres following the ID3-Tags. Additionally the User has the ability to change this system and to create his own view according to his point of interest. That means that he is able to change the main genres and the sub-genres as well. As there are always different views especially on the terms of sub-genres the user is even allowed to change the structure of the given categories. That means that one is permitted to extract an ambiguous sub-genre to directly attach it to another genre. This will be able via the right middle window showing the sub-genres and their actual belongings as well. Probably the system will give the possibility to cancel unmeant genres at all.

=== Visual Mapping ===

*Due to the enormous number of music-genres ID3-Tags provide we will probably choose a tree structure in addition to the text-based genre-navigator on the right in lower levels. This structure should be similar to a hyperbolic tree.

Dimension "Genre-Multitude" --> visual attribute "Tree Branches"

*To afford best visualization we are right now not sure whether we should display the percentage-rate in bars as shown in the fake-screenshot below, or should rather use a Bubble-Chart, where the size of the bubbles is determined by the values of occurrence per genre.

Dimension "File-Occurrence" --> visual attribute "Area"

=== Specification of used Techniques / applied Principles ===

*Hyperbolic Trees:
(s. slide 18, 19, 20 of Info_Vis4.pdf handed out in the course “188.305 VO Informationsvisualisierung”)

Due to the enormous number of music-genres ID-3 Tags provide we probably choose a tree structure
similar to a hyperbolic tree to visualize the raw data extracted from the corresponding mp3-archiv.

*Bubble Chart:
(s. similar to bubble-maps, slide 52 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung, http://peltiertech.com/Excel/ChartsHowTo/HowToBubble.html)

Bubble chart, where the size of the bubbles is determined by the values of occurrence.

*Details on demand:
(s. slide 55 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Used several times. (File-Explorer, Genre-Information, ...)

*Linking & Brushing:
(s. slide 100 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Detail Windows containing percentage-distribution according to the specific genre

*Focus & Context: Tiled Multi-Level Browser
(slide 69 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Overview Window, Details on Demand Window

=== Possibilities of User-Interaction ===

* Assemble information about occurence-percentage per genre
* Assemble information according to ID3-Tags
* Group genres
* Build paths
* Build new tree
* Modify leaves
* Save play-list

=== Mockup / Fake Screenshot ===

[[Image:MP3_Viewer.gif]]

== References ==

[Wikipedia, 2005a] ID3, Wikipedia, Last updated: 21 November, 2005, Retrieved at: November 22, 2005, http://www.csam.montclair.edu/~mcdougal/SCP/D_types.htm

[Wikipedia, 2005b] MP3, Wikipedia, Last updated: 21 November, 2005, Retrieved at: November 22, 2005, http://en.wikipedia.org/wiki/Mp3

[Id3.org, 2004] ID3v2 frames, Id3.org, Last updated: 28. Februar, 2004, Retrieved at: November 22, 2005, http://www.id3.org/frames.html

[Montclaire, 2000] Data Types, Department of Science and Mathematics at Montclair State University, Last updated: 3. August, 2000, Retrieved at: November 22, 2005, http://www.csam.montclair.edu/~mcdougal/SCP/D_types.htm

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 3 - Design

2005-12-02T09:39:44Z

UE-InfoVis0506 0026030: /* Which tasks should be solved? */

== Topic ==
'''MP3 Archive Visualization'''
== Specification of the Application Area and the given Dataset ==
=== Application area Analysis ===

The application area in this case is the creation of a clearly arranged visualization for a big music archive consisting of several thousand files. This can be achieved by using the values of the ID3-tags as well as the attributes of the music files itself.

Since the existing data mainly consist of discrete values, our mission to produce an appropriate visualization will basically be achieved with the creation of quantitative data by using a reasonable combination of given values.

Most Audio formats nowadays use the container format ID3 to store additional Information about the file, apart from the given file properties.

ID3 generally supports loads of different input values and to visualize them all would go beyond the scope of our task. Besides in most cases only the most common values like Album, Interpret, Year… are specified correctly.
Therefore we will concentrate our work for our prototype on these entries.

=== Dataset Analysis ===

{|
|- style="background:#FF6633"
!Attribute
!Data type
!Description
|-
|Title
|Discrete
|Name of the Song
|- style="background:#FFCC99"
|Interpret
|Discrete
|Name of the Artist/Band who plays the song
|-
|Album
|Discrete
|Name of the Album/Compilation the song belongs to
|- style="background:#FFCC99"
|Year
|Ordinal
|Year the song was recorded in
|-
|Genre
|Nominal
|Genre the song belongs to
|- style="background:#FFCC99"
|Size
|Quantitative
|Size of the file
|-
|Duration
|Quantitative
|Overall play length of the song
|- style="background:#FFCC99"
|Bit rate
|Ordinal
|Bit rate the file was encoded with
|-
|Sample rate
|Ordinal
|Sample rate the file was encoded with
|- style="background:#FFCC99"
|File Location
|Discrete
|Full system path to the file
|}

A complete data record consists of all attributes listed above.

All data in this set is one-dimensional.

== Analysis of the Target User Group ==

=== Who should use this kind of visualization technique? ===

This visualization technique is mainly meant for the 'end-users', that is someone who collects lots of MP3s. With 'lots of MP3s' we mean quite a few GBs, just more than 30 GBs. Our visualization should help the user to get an overview of his collection and his listening-habits.
This visualization technique could also be interesting for the band and the music industry, if they want to produce a new album. for example: a band (like radiohead) who changed their musicstyle over the years and wants to know which style is preferd more. But therefor they have to compare these dates from many users.

=== What are the characteristics of the target group? ===

People of this group are music enthusiasts. They have thousands of mp3s on their harddisk and love it to collect them. Most of them have lost track of their collection, on the strength of the abundance of their collection. These people mainly receive their mp3s from the internet instead of buying CDs, because they like to see their whole musiccollection at a glance.

=== Are there any known / often used Methods / Visualisation Techniques? ===

No, we don't know any similar visualization technique. ITunes only shows textbasedly how often a song was heard.

== Intended Purpose of our Visualization ==

=== What should be achieved with this visualization? ===
A better information representation of the MP3s should be achieved. Our visualization should help the user to get an overview of his collection and his listening-habits. He will get informations to the tracks, in reference to a special artist, which he often listens to and to those which he has never heard before. The representation of the data should be expressive, precise and self-explanatory.

=== Which tasks should be solved? ===
By using this visualization technique, the user will get informations to one chosen artist and whose discography. For example: in his database the user has got the band radiohead, who produced albums over 15 years and in this period they changed their musicstyle from alternative rock to experimental electronic. The visualization will show him from which producing period he has got more MP3s and which period he likes more, by counting the number of listenings of each song. The result could be that he has got more MP3s from their early years, but likes the experimental electronic tracks more.

=== Questions that should be solved with this visualization technique ===
It should be possible to get the frequency distribution of the mp3s relating to their characteristics. But the precondition therefor is that the ID3-tags are available

== Proposal of Design ==

=== Types of Visualization applied ===

As there are no comparable features implemented in existing mp3 archival storage systems we decided to implement a visualization kit exploring the quantitative distribution of an mp3-archive according to the different genres. As a default the main window shows several genres the authors considered to be the right ones for music main-genres in the root directory including their percentage-rate corresponding to the whole file-archive.

By a single click on one of the main genres the user gets additional information in the upper right window on the one hand and of course a file-listing in the lower left window on the other hand. Double clicking a genre in the main window brings the user straight to a sub-level containing the corresponding sub-genres again shown in the main window. Through the path shown on top of the main window the system allows to easily jump back to wherever necessary out of every level.

Further on the user is able to read out file-specific information like Title, Interpret,
Album, Year, Duration & Bit-Rate through a click on the particular file listed below, but is also allowed to save a play-list from the files selected via the file-explorer.

Following feature turns the visualization kit into a rather complex and outstanding tool. By default the tool sorts the input data through a given hierarchy according to the main genres and alternatively to the sub-genres following the ID3-Tags. Additionally the User has the ability to change this system and to create his own view according to his point of interest. That means that he is able to change the main genres and the sub-genres as well. As there are always different views especially on the terms of sub-genres the user is even allowed to change the structure of the given categories. That means that one is permitted to extract an ambiguous sub-genre to directly attach it to another genre. This will be able via the right middle window showing the sub-genres and their actual belongings as well. Probably the system will give the possibility to cancel unmeant genres at all.

=== Visual Mapping ===

*Due to the enormous number of music-genres ID3-Tags provide we will probably choose a tree structure in addition to the text-based genre-navigator on the right in lower levels. This structure should be similar to a hyperbolic tree.

Dimension "Genre-Multitude" --> visual attribute "Tree Branches"

*To afford best visualization we are right now not sure whether we should display the percentage-rate in bars as shown in the fake-screenshot below, or should rather use a Bubble-Chart, where the size of the bubbles is determined by the values of occurrence per genre.

Dimension "File-Occurrence" --> visual attribute "Area"

=== Specification of used Techniques / applied Principles ===

*Hyperbolic Trees:
(s. slide 18, 19, 20 of Info_Vis4.pdf handed out in the course “188.305 VO Informationsvisualisierung”)

Due to the enormous number of music-genres ID-3 Tags provide we probably choose a tree structure
similar to a hyperbolic tree to visualize the raw data extracted from the corresponding mp3-archiv.

*Bubble Chart:
(s. similar to bubble-maps, slide 52 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung, http://peltiertech.com/Excel/ChartsHowTo/HowToBubble.html)

Bubble chart, where the size of the bubbles is determined by the values of occurrence.

*Details on demand:
(s. slide 55 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Used several times. (File-Explorer, Genre-Information, ...)

*Linking & Brushing:
(s. slide 100 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Detail Windows containing percentage-distribution according to the specific genre

*Focus & Context: Tiled Multi-Level Browser
(slide 69 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Overview Window, Details on Demand Window

=== Possibilities of User-Interaction ===

* Assemble information about occurence-percentage per genre
* Assemble information according to ID3-Tags
* Group genres
* Build paths
* Build new tree
* Modify leaves
* Save play-list

=== Mockup / Fake Screenshot ===

[[Image:MP3_Viewer.gif]]

== References ==

[Wikipedia, 2005a] ID3, Wikipedia, Last updated: 21 November, 2005, Retrieved at: November 22, 2005, http://www.csam.montclair.edu/~mcdougal/SCP/D_types.htm

[Wikipedia, 2005b] MP3, Wikipedia, Last updated: 21 November, 2005, Retrieved at: November 22, 2005, http://en.wikipedia.org/wiki/Mp3

[Id3.org, 2004] ID3v2 frames, Id3.org, Last updated: 28. Februar, 2004, Retrieved at: November 22, 2005, http://www.id3.org/frames.html

[Montclaire, 2000] Data Types, Department of Science and Mathematics at Montclair State University, Last updated: 3. August, 2000, Retrieved at: November 22, 2005, http://www.csam.montclair.edu/~mcdougal/SCP/D_types.htm

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 3 - Design

2005-12-02T09:39:34Z

UE-InfoVis0506 0026030: /* Who should use this kind of visualization technique? */

== Topic ==
'''MP3 Archive Visualization'''
== Specification of the Application Area and the given Dataset ==
=== Application area Analysis ===

The application area in this case is the creation of a clearly arranged visualization for a big music archive consisting of several thousand files. This can be achieved by using the values of the ID3-tags as well as the attributes of the music files itself.

Since the existing data mainly consist of discrete values, our mission to produce an appropriate visualization will basically be achieved with the creation of quantitative data by using a reasonable combination of given values.

Most Audio formats nowadays use the container format ID3 to store additional Information about the file, apart from the given file properties.

ID3 generally supports loads of different input values and to visualize them all would go beyond the scope of our task. Besides in most cases only the most common values like Album, Interpret, Year… are specified correctly.
Therefore we will concentrate our work for our prototype on these entries.

=== Dataset Analysis ===

{|
|- style="background:#FF6633"
!Attribute
!Data type
!Description
|-
|Title
|Discrete
|Name of the Song
|- style="background:#FFCC99"
|Interpret
|Discrete
|Name of the Artist/Band who plays the song
|-
|Album
|Discrete
|Name of the Album/Compilation the song belongs to
|- style="background:#FFCC99"
|Year
|Ordinal
|Year the song was recorded in
|-
|Genre
|Nominal
|Genre the song belongs to
|- style="background:#FFCC99"
|Size
|Quantitative
|Size of the file
|-
|Duration
|Quantitative
|Overall play length of the song
|- style="background:#FFCC99"
|Bit rate
|Ordinal
|Bit rate the file was encoded with
|-
|Sample rate
|Ordinal
|Sample rate the file was encoded with
|- style="background:#FFCC99"
|File Location
|Discrete
|Full system path to the file
|}

A complete data record consists of all attributes listed above.

All data in this set is one-dimensional.

== Analysis of the Target User Group ==

=== Who should use this kind of visualization technique? ===

This visualization technique is mainly meant for the 'end-users', that is someone who collects lots of MP3s. With 'lots of MP3s' we mean quite a few GBs, just more than 30 GBs. Our visualization should help the user to get an overview of his collection and his listening-habits.
This visualization technique could also be interesting for the band and the music industry, if they want to produce a new album. for example: a band (like radiohead) who changed their musicstyle over the years and wants to know which style is preferd more. But therefor they have to compare these dates from many users.

=== What are the characteristics of the target group? ===

People of this group are music enthusiasts. They have thousands of mp3s on their harddisk and love it to collect them. Most of them have lost track of their collection, on the strength of the abundance of their collection. These people mainly receive their mp3s from the internet instead of buying CDs, because they like to see their whole musiccollection at a glance.

=== Are there any known / often used Methods / Visualisation Techniques? ===

No, we don't know any similar visualization technique. ITunes only shows textbasedly how often a song was heard.

== Intended Purpose of our Visualization ==

=== What should be achieved with this visualization? ===
A better information representation of the MP3s should be achieved. Our visualization should help the user to get an overview of his collection and his listening-habits. He will get informations to the tracks, in reference to a special artist, which he often listens to and to those which he has never heard before. The representation of the data should be expressive, precise and self-explanatory.

=== Which tasks should be solved? ===
The data readout of contentspecific characteristics should be possible.
We want to visualize the distribution of the genres, so that the user is able to group them how he likes. e.g.: there is a group called 'classics' and the user wants to put the group 'oldies' to them, so he can make a subgroup.

=== Questions that should be solved with this visualization technique ===
It should be possible to get the frequency distribution of the mp3s relating to their characteristics. But the precondition therefor is that the ID3-tags are available

== Proposal of Design ==

=== Types of Visualization applied ===

As there are no comparable features implemented in existing mp3 archival storage systems we decided to implement a visualization kit exploring the quantitative distribution of an mp3-archive according to the different genres. As a default the main window shows several genres the authors considered to be the right ones for music main-genres in the root directory including their percentage-rate corresponding to the whole file-archive.

By a single click on one of the main genres the user gets additional information in the upper right window on the one hand and of course a file-listing in the lower left window on the other hand. Double clicking a genre in the main window brings the user straight to a sub-level containing the corresponding sub-genres again shown in the main window. Through the path shown on top of the main window the system allows to easily jump back to wherever necessary out of every level.

Further on the user is able to read out file-specific information like Title, Interpret,
Album, Year, Duration & Bit-Rate through a click on the particular file listed below, but is also allowed to save a play-list from the files selected via the file-explorer.

Following feature turns the visualization kit into a rather complex and outstanding tool. By default the tool sorts the input data through a given hierarchy according to the main genres and alternatively to the sub-genres following the ID3-Tags. Additionally the User has the ability to change this system and to create his own view according to his point of interest. That means that he is able to change the main genres and the sub-genres as well. As there are always different views especially on the terms of sub-genres the user is even allowed to change the structure of the given categories. That means that one is permitted to extract an ambiguous sub-genre to directly attach it to another genre. This will be able via the right middle window showing the sub-genres and their actual belongings as well. Probably the system will give the possibility to cancel unmeant genres at all.

=== Visual Mapping ===

*Due to the enormous number of music-genres ID3-Tags provide we will probably choose a tree structure in addition to the text-based genre-navigator on the right in lower levels. This structure should be similar to a hyperbolic tree.

Dimension "Genre-Multitude" --> visual attribute "Tree Branches"

*To afford best visualization we are right now not sure whether we should display the percentage-rate in bars as shown in the fake-screenshot below, or should rather use a Bubble-Chart, where the size of the bubbles is determined by the values of occurrence per genre.

Dimension "File-Occurrence" --> visual attribute "Area"

=== Specification of used Techniques / applied Principles ===

*Hyperbolic Trees:
(s. slide 18, 19, 20 of Info_Vis4.pdf handed out in the course “188.305 VO Informationsvisualisierung”)

Due to the enormous number of music-genres ID-3 Tags provide we probably choose a tree structure
similar to a hyperbolic tree to visualize the raw data extracted from the corresponding mp3-archiv.

*Bubble Chart:
(s. similar to bubble-maps, slide 52 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung, http://peltiertech.com/Excel/ChartsHowTo/HowToBubble.html)

Bubble chart, where the size of the bubbles is determined by the values of occurrence.

*Details on demand:
(s. slide 55 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Used several times. (File-Explorer, Genre-Information, ...)

*Linking & Brushing:
(s. slide 100 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Detail Windows containing percentage-distribution according to the specific genre

*Focus & Context: Tiled Multi-Level Browser
(slide 69 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Overview Window, Details on Demand Window

=== Possibilities of User-Interaction ===

* Assemble information about occurence-percentage per genre
* Assemble information according to ID3-Tags
* Group genres
* Build paths
* Build new tree
* Modify leaves
* Save play-list

=== Mockup / Fake Screenshot ===

[[Image:MP3_Viewer.gif]]

== References ==

[Wikipedia, 2005a] ID3, Wikipedia, Last updated: 21 November, 2005, Retrieved at: November 22, 2005, http://www.csam.montclair.edu/~mcdougal/SCP/D_types.htm

[Wikipedia, 2005b] MP3, Wikipedia, Last updated: 21 November, 2005, Retrieved at: November 22, 2005, http://en.wikipedia.org/wiki/Mp3

[Id3.org, 2004] ID3v2 frames, Id3.org, Last updated: 28. Februar, 2004, Retrieved at: November 22, 2005, http://www.id3.org/frames.html

[Montclaire, 2000] Data Types, Department of Science and Mathematics at Montclair State University, Last updated: 3. August, 2000, Retrieved at: November 22, 2005, http://www.csam.montclair.edu/~mcdougal/SCP/D_types.htm

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 3 - Design

2005-12-02T09:38:35Z

UE-InfoVis0506 0026030: /* What should be achieved with this visualization? */

== Topic ==
'''MP3 Archive Visualization'''
== Specification of the Application Area and the given Dataset ==
=== Application area Analysis ===

The application area in this case is the creation of a clearly arranged visualization for a big music archive consisting of several thousand files. This can be achieved by using the values of the ID3-tags as well as the attributes of the music files itself.

Since the existing data mainly consist of discrete values, our mission to produce an appropriate visualization will basically be achieved with the creation of quantitative data by using a reasonable combination of given values.

Most Audio formats nowadays use the container format ID3 to store additional Information about the file, apart from the given file properties.

ID3 generally supports loads of different input values and to visualize them all would go beyond the scope of our task. Besides in most cases only the most common values like Album, Interpret, Year… are specified correctly.
Therefore we will concentrate our work for our prototype on these entries.

=== Dataset Analysis ===

{|
|- style="background:#FF6633"
!Attribute
!Data type
!Description
|-
|Title
|Discrete
|Name of the Song
|- style="background:#FFCC99"
|Interpret
|Discrete
|Name of the Artist/Band who plays the song
|-
|Album
|Discrete
|Name of the Album/Compilation the song belongs to
|- style="background:#FFCC99"
|Year
|Ordinal
|Year the song was recorded in
|-
|Genre
|Nominal
|Genre the song belongs to
|- style="background:#FFCC99"
|Size
|Quantitative
|Size of the file
|-
|Duration
|Quantitative
|Overall play length of the song
|- style="background:#FFCC99"
|Bit rate
|Ordinal
|Bit rate the file was encoded with
|-
|Sample rate
|Ordinal
|Sample rate the file was encoded with
|- style="background:#FFCC99"
|File Location
|Discrete
|Full system path to the file
|}

A complete data record consists of all attributes listed above.

All data in this set is one-dimensional.

== Analysis of the Target User Group ==

=== Who should use this kind of visualization technique? ===

This visualization technique is mainly meant for the 'end-users', that is someone who collects lots of MP3s. With 'lots of MP3s' we mean quite a few GBs, just more than 30 GBs. Our visualization should help the user to get an overview of his collection and his listening-habits. By using this visualization technique, the user will get informations to one chosen artist and whose discography. For example: in his database the user has got the band radiohead, who produced albums over 15 years and in this period they changed their musicstyle from alternative rock to experimental electronic. The visualization will show him from which producing period he has got more MP3s and which period he likes more, by counting the number of listenings of each song. The result could be that he has got more MP3s from their early years, but likes the experimental electronic tracks more.
This visualization technique could also be interesting for the band and the music industry, if they want to produce a new album. for example: a band (like radiohead) who changed their musicstyle over the years and wants to know which style is preferd more. But therefor they have to compare these dates from many users.

=== What are the characteristics of the target group? ===

People of this group are music enthusiasts. They have thousands of mp3s on their harddisk and love it to collect them. Most of them have lost track of their collection, on the strength of the abundance of their collection. These people mainly receive their mp3s from the internet instead of buying CDs, because they like to see their whole musiccollection at a glance.

=== Are there any known / often used Methods / Visualisation Techniques? ===

No, we don't know any similar visualization technique. ITunes only shows textbasedly how often a song was heard.

== Intended Purpose of our Visualization ==

=== What should be achieved with this visualization? ===
A better information representation of the MP3s should be achieved. Our visualization should help the user to get an overview of his collection and his listening-habits. He will get informations to the tracks, in reference to a special artist, which he often listens to and to those which he has never heard before. The representation of the data should be expressive, precise and self-explanatory.

=== Which tasks should be solved? ===
The data readout of contentspecific characteristics should be possible.
We want to visualize the distribution of the genres, so that the user is able to group them how he likes. e.g.: there is a group called 'classics' and the user wants to put the group 'oldies' to them, so he can make a subgroup.

=== Questions that should be solved with this visualization technique ===
It should be possible to get the frequency distribution of the mp3s relating to their characteristics. But the precondition therefor is that the ID3-tags are available

== Proposal of Design ==

=== Types of Visualization applied ===

As there are no comparable features implemented in existing mp3 archival storage systems we decided to implement a visualization kit exploring the quantitative distribution of an mp3-archive according to the different genres. As a default the main window shows several genres the authors considered to be the right ones for music main-genres in the root directory including their percentage-rate corresponding to the whole file-archive.

By a single click on one of the main genres the user gets additional information in the upper right window on the one hand and of course a file-listing in the lower left window on the other hand. Double clicking a genre in the main window brings the user straight to a sub-level containing the corresponding sub-genres again shown in the main window. Through the path shown on top of the main window the system allows to easily jump back to wherever necessary out of every level.

Further on the user is able to read out file-specific information like Title, Interpret,
Album, Year, Duration & Bit-Rate through a click on the particular file listed below, but is also allowed to save a play-list from the files selected via the file-explorer.

Following feature turns the visualization kit into a rather complex and outstanding tool. By default the tool sorts the input data through a given hierarchy according to the main genres and alternatively to the sub-genres following the ID3-Tags. Additionally the User has the ability to change this system and to create his own view according to his point of interest. That means that he is able to change the main genres and the sub-genres as well. As there are always different views especially on the terms of sub-genres the user is even allowed to change the structure of the given categories. That means that one is permitted to extract an ambiguous sub-genre to directly attach it to another genre. This will be able via the right middle window showing the sub-genres and their actual belongings as well. Probably the system will give the possibility to cancel unmeant genres at all.

=== Visual Mapping ===

*Due to the enormous number of music-genres ID3-Tags provide we will probably choose a tree structure in addition to the text-based genre-navigator on the right in lower levels. This structure should be similar to a hyperbolic tree.

Dimension "Genre-Multitude" --> visual attribute "Tree Branches"

*To afford best visualization we are right now not sure whether we should display the percentage-rate in bars as shown in the fake-screenshot below, or should rather use a Bubble-Chart, where the size of the bubbles is determined by the values of occurrence per genre.

Dimension "File-Occurrence" --> visual attribute "Area"

=== Specification of used Techniques / applied Principles ===

*Hyperbolic Trees:
(s. slide 18, 19, 20 of Info_Vis4.pdf handed out in the course “188.305 VO Informationsvisualisierung”)

Due to the enormous number of music-genres ID-3 Tags provide we probably choose a tree structure
similar to a hyperbolic tree to visualize the raw data extracted from the corresponding mp3-archiv.

*Bubble Chart:
(s. similar to bubble-maps, slide 52 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung, http://peltiertech.com/Excel/ChartsHowTo/HowToBubble.html)

Bubble chart, where the size of the bubbles is determined by the values of occurrence.

*Details on demand:
(s. slide 55 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Used several times. (File-Explorer, Genre-Information, ...)

*Linking & Brushing:
(s. slide 100 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Detail Windows containing percentage-distribution according to the specific genre

*Focus & Context: Tiled Multi-Level Browser
(slide 69 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Overview Window, Details on Demand Window

=== Possibilities of User-Interaction ===

* Assemble information about occurence-percentage per genre
* Assemble information according to ID3-Tags
* Group genres
* Build paths
* Build new tree
* Modify leaves
* Save play-list

=== Mockup / Fake Screenshot ===

[[Image:MP3_Viewer.gif]]

== References ==

[Wikipedia, 2005a] ID3, Wikipedia, Last updated: 21 November, 2005, Retrieved at: November 22, 2005, http://www.csam.montclair.edu/~mcdougal/SCP/D_types.htm

[Wikipedia, 2005b] MP3, Wikipedia, Last updated: 21 November, 2005, Retrieved at: November 22, 2005, http://en.wikipedia.org/wiki/Mp3

[Id3.org, 2004] ID3v2 frames, Id3.org, Last updated: 28. Februar, 2004, Retrieved at: November 22, 2005, http://www.id3.org/frames.html

[Montclaire, 2000] Data Types, Department of Science and Mathematics at Montclair State University, Last updated: 3. August, 2000, Retrieved at: November 22, 2005, http://www.csam.montclair.edu/~mcdougal/SCP/D_types.htm

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 3 - Design

2005-12-02T09:37:06Z

UE-InfoVis0506 0026030: /* What should be achieved with this visualization? */

== Topic ==
'''MP3 Archive Visualization'''
== Specification of the Application Area and the given Dataset ==
=== Application area Analysis ===

The application area in this case is the creation of a clearly arranged visualization for a big music archive consisting of several thousand files. This can be achieved by using the values of the ID3-tags as well as the attributes of the music files itself.

Since the existing data mainly consist of discrete values, our mission to produce an appropriate visualization will basically be achieved with the creation of quantitative data by using a reasonable combination of given values.

Most Audio formats nowadays use the container format ID3 to store additional Information about the file, apart from the given file properties.

ID3 generally supports loads of different input values and to visualize them all would go beyond the scope of our task. Besides in most cases only the most common values like Album, Interpret, Year… are specified correctly.
Therefore we will concentrate our work for our prototype on these entries.

=== Dataset Analysis ===

{|
|- style="background:#FF6633"
!Attribute
!Data type
!Description
|-
|Title
|Discrete
|Name of the Song
|- style="background:#FFCC99"
|Interpret
|Discrete
|Name of the Artist/Band who plays the song
|-
|Album
|Discrete
|Name of the Album/Compilation the song belongs to
|- style="background:#FFCC99"
|Year
|Ordinal
|Year the song was recorded in
|-
|Genre
|Nominal
|Genre the song belongs to
|- style="background:#FFCC99"
|Size
|Quantitative
|Size of the file
|-
|Duration
|Quantitative
|Overall play length of the song
|- style="background:#FFCC99"
|Bit rate
|Ordinal
|Bit rate the file was encoded with
|-
|Sample rate
|Ordinal
|Sample rate the file was encoded with
|- style="background:#FFCC99"
|File Location
|Discrete
|Full system path to the file
|}

A complete data record consists of all attributes listed above.

All data in this set is one-dimensional.

== Analysis of the Target User Group ==

=== Who should use this kind of visualization technique? ===

This visualization technique is mainly meant for the 'end-users', that is someone who collects lots of MP3s. With 'lots of MP3s' we mean quite a few GBs, just more than 30 GBs. Our visualization should help the user to get an overview of his collection and his listening-habits. By using this visualization technique, the user will get informations to one chosen artist and whose discography. For example: in his database the user has got the band radiohead, who produced albums over 15 years and in this period they changed their musicstyle from alternative rock to experimental electronic. The visualization will show him from which producing period he has got more MP3s and which period he likes more, by counting the number of listenings of each song. The result could be that he has got more MP3s from their early years, but likes the experimental electronic tracks more.
This visualization technique could also be interesting for the band and the music industry, if they want to produce a new album. for example: a band (like radiohead) who changed their musicstyle over the years and wants to know which style is preferd more. But therefor they have to compare these dates from many users.

=== What are the characteristics of the target group? ===

People of this group are music enthusiasts. They have thousands of mp3s on their harddisk and love it to collect them. Most of them have lost track of their collection, on the strength of the abundance of their collection. These people mainly receive their mp3s from the internet instead of buying CDs, because they like to see their whole musiccollection at a glance.

=== Are there any known / often used Methods / Visualisation Techniques? ===

No, we don't know any similar visualization technique. ITunes only shows textbasedly how often a song was heard.

== Intended Purpose of our Visualization ==

=== What should be achieved with this visualization? ===
A better information representation of the MP3s should be achieved. Our visualization should help the user to get an overview of his collection and his listening-habits. He will get informations to the tracks which he often listens to and to those which he has never heard befor. The representation of the data should be expressive, precise and self-explanatory.

By using this visualization technique, the user will get informations to one chosen artist and whose discography.

=== Which tasks should be solved? ===
The data readout of contentspecific characteristics should be possible.
We want to visualize the distribution of the genres, so that the user is able to group them how he likes. e.g.: there is a group called 'classics' and the user wants to put the group 'oldies' to them, so he can make a subgroup.

=== Questions that should be solved with this visualization technique ===
It should be possible to get the frequency distribution of the mp3s relating to their characteristics. But the precondition therefor is that the ID3-tags are available

== Proposal of Design ==

=== Types of Visualization applied ===

As there are no comparable features implemented in existing mp3 archival storage systems we decided to implement a visualization kit exploring the quantitative distribution of an mp3-archive according to the different genres. As a default the main window shows several genres the authors considered to be the right ones for music main-genres in the root directory including their percentage-rate corresponding to the whole file-archive.

By a single click on one of the main genres the user gets additional information in the upper right window on the one hand and of course a file-listing in the lower left window on the other hand. Double clicking a genre in the main window brings the user straight to a sub-level containing the corresponding sub-genres again shown in the main window. Through the path shown on top of the main window the system allows to easily jump back to wherever necessary out of every level.

Further on the user is able to read out file-specific information like Title, Interpret,
Album, Year, Duration & Bit-Rate through a click on the particular file listed below, but is also allowed to save a play-list from the files selected via the file-explorer.

Following feature turns the visualization kit into a rather complex and outstanding tool. By default the tool sorts the input data through a given hierarchy according to the main genres and alternatively to the sub-genres following the ID3-Tags. Additionally the User has the ability to change this system and to create his own view according to his point of interest. That means that he is able to change the main genres and the sub-genres as well. As there are always different views especially on the terms of sub-genres the user is even allowed to change the structure of the given categories. That means that one is permitted to extract an ambiguous sub-genre to directly attach it to another genre. This will be able via the right middle window showing the sub-genres and their actual belongings as well. Probably the system will give the possibility to cancel unmeant genres at all.

=== Visual Mapping ===

*Due to the enormous number of music-genres ID3-Tags provide we will probably choose a tree structure in addition to the text-based genre-navigator on the right in lower levels. This structure should be similar to a hyperbolic tree.

Dimension "Genre-Multitude" --> visual attribute "Tree Branches"

*To afford best visualization we are right now not sure whether we should display the percentage-rate in bars as shown in the fake-screenshot below, or should rather use a Bubble-Chart, where the size of the bubbles is determined by the values of occurrence per genre.

Dimension "File-Occurrence" --> visual attribute "Area"

=== Specification of used Techniques / applied Principles ===

*Hyperbolic Trees:
(s. slide 18, 19, 20 of Info_Vis4.pdf handed out in the course “188.305 VO Informationsvisualisierung”)

Due to the enormous number of music-genres ID-3 Tags provide we probably choose a tree structure
similar to a hyperbolic tree to visualize the raw data extracted from the corresponding mp3-archiv.

*Bubble Chart:
(s. similar to bubble-maps, slide 52 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung, http://peltiertech.com/Excel/ChartsHowTo/HowToBubble.html)

Bubble chart, where the size of the bubbles is determined by the values of occurrence.

*Details on demand:
(s. slide 55 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Used several times. (File-Explorer, Genre-Information, ...)

*Linking & Brushing:
(s. slide 100 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Detail Windows containing percentage-distribution according to the specific genre

*Focus & Context: Tiled Multi-Level Browser
(slide 69 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Overview Window, Details on Demand Window

=== Possibilities of User-Interaction ===

* Assemble information about occurence-percentage per genre
* Assemble information according to ID3-Tags
* Group genres
* Build paths
* Build new tree
* Modify leaves
* Save play-list

=== Mockup / Fake Screenshot ===

[[Image:MP3_Viewer.gif]]

== References ==

[Wikipedia, 2005a] ID3, Wikipedia, Last updated: 21 November, 2005, Retrieved at: November 22, 2005, http://www.csam.montclair.edu/~mcdougal/SCP/D_types.htm

[Wikipedia, 2005b] MP3, Wikipedia, Last updated: 21 November, 2005, Retrieved at: November 22, 2005, http://en.wikipedia.org/wiki/Mp3

[Id3.org, 2004] ID3v2 frames, Id3.org, Last updated: 28. Februar, 2004, Retrieved at: November 22, 2005, http://www.id3.org/frames.html

[Montclaire, 2000] Data Types, Department of Science and Mathematics at Montclair State University, Last updated: 3. August, 2000, Retrieved at: November 22, 2005, http://www.csam.montclair.edu/~mcdougal/SCP/D_types.htm

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 3 - Design

2005-12-02T09:34:43Z

UE-InfoVis0506 0026030: /* What should be achieved with this visualization? */

== Topic ==
'''MP3 Archive Visualization'''
== Specification of the Application Area and the given Dataset ==
=== Application area Analysis ===

The application area in this case is the creation of a clearly arranged visualization for a big music archive consisting of several thousand files. This can be achieved by using the values of the ID3-tags as well as the attributes of the music files itself.

Since the existing data mainly consist of discrete values, our mission to produce an appropriate visualization will basically be achieved with the creation of quantitative data by using a reasonable combination of given values.

Most Audio formats nowadays use the container format ID3 to store additional Information about the file, apart from the given file properties.

ID3 generally supports loads of different input values and to visualize them all would go beyond the scope of our task. Besides in most cases only the most common values like Album, Interpret, Year… are specified correctly.
Therefore we will concentrate our work for our prototype on these entries.

=== Dataset Analysis ===

{|
|- style="background:#FF6633"
!Attribute
!Data type
!Description
|-
|Title
|Discrete
|Name of the Song
|- style="background:#FFCC99"
|Interpret
|Discrete
|Name of the Artist/Band who plays the song
|-
|Album
|Discrete
|Name of the Album/Compilation the song belongs to
|- style="background:#FFCC99"
|Year
|Ordinal
|Year the song was recorded in
|-
|Genre
|Nominal
|Genre the song belongs to
|- style="background:#FFCC99"
|Size
|Quantitative
|Size of the file
|-
|Duration
|Quantitative
|Overall play length of the song
|- style="background:#FFCC99"
|Bit rate
|Ordinal
|Bit rate the file was encoded with
|-
|Sample rate
|Ordinal
|Sample rate the file was encoded with
|- style="background:#FFCC99"
|File Location
|Discrete
|Full system path to the file
|}

A complete data record consists of all attributes listed above.

All data in this set is one-dimensional.

== Analysis of the Target User Group ==

=== Who should use this kind of visualization technique? ===

This visualization technique is mainly meant for the 'end-users', that is someone who collects lots of MP3s. With 'lots of MP3s' we mean quite a few GBs, just more than 30 GBs. Our visualization should help the user to get an overview of his collection and his listening-habits. By using this visualization technique, the user will get informations to one chosen artist and whose discography. For example: in his database the user has got the band radiohead, who produced albums over 15 years and in this period they changed their musicstyle from alternative rock to experimental electronic. The visualization will show him from which producing period he has got more MP3s and which period he likes more, by counting the number of listenings of each song. The result could be that he has got more MP3s from their early years, but likes the experimental electronic tracks more.
This visualization technique could also be interesting for the band and the music industry, if they want to produce a new album. for example: a band (like radiohead) who changed their musicstyle over the years and wants to know which style is preferd more. But therefor they have to compare these dates from many users.

=== What are the characteristics of the target group? ===

People of this group are music enthusiasts. They have thousands of mp3s on their harddisk and love it to collect them. Most of them have lost track of their collection, on the strength of the abundance of their collection. These people mainly receive their mp3s from the internet instead of buying CDs, because they like to see their whole musiccollection at a glance.

=== Are there any known / often used Methods / Visualisation Techniques? ===

No, we don't know any similar visualization technique. ITunes only shows textbasedly how often a song was heard.

== Intended Purpose of our Visualization ==

=== What should be achieved with this visualization? ===
A better information representation of the MP3s should be achieved. So that the user could get a good overview of the tracks which he often hears and those which he has never listened to. The representation of the data should be expressive, precise and self-explanatory.

=== Which tasks should be solved? ===
The data readout of contentspecific characteristics should be possible.
We want to visualize the distribution of the genres, so that the user is able to group them how he likes. e.g.: there is a group called 'classics' and the user wants to put the group 'oldies' to them, so he can make a subgroup.

=== Questions that should be solved with this visualization technique ===
It should be possible to get the frequency distribution of the mp3s relating to their characteristics. But the precondition therefor is that the ID3-tags are available

== Proposal of Design ==

=== Types of Visualization applied ===

As there are no comparable features implemented in existing mp3 archival storage systems we decided to implement a visualization kit exploring the quantitative distribution of an mp3-archive according to the different genres. As a default the main window shows several genres the authors considered to be the right ones for music main-genres in the root directory including their percentage-rate corresponding to the whole file-archive.

By a single click on one of the main genres the user gets additional information in the upper right window on the one hand and of course a file-listing in the lower left window on the other hand. Double clicking a genre in the main window brings the user straight to a sub-level containing the corresponding sub-genres again shown in the main window. Through the path shown on top of the main window the system allows to easily jump back to wherever necessary out of every level.

Further on the user is able to read out file-specific information like Title, Interpret,
Album, Year, Duration & Bit-Rate through a click on the particular file listed below, but is also allowed to save a play-list from the files selected via the file-explorer.

Following feature turns the visualization kit into a rather complex and outstanding tool. By default the tool sorts the input data through a given hierarchy according to the main genres and alternatively to the sub-genres following the ID3-Tags. Additionally the User has the ability to change this system and to create his own view according to his point of interest. That means that he is able to change the main genres and the sub-genres as well. As there are always different views especially on the terms of sub-genres the user is even allowed to change the structure of the given categories. That means that one is permitted to extract an ambiguous sub-genre to directly attach it to another genre. This will be able via the right middle window showing the sub-genres and their actual belongings as well. Probably the system will give the possibility to cancel unmeant genres at all.

=== Visual Mapping ===

*Due to the enormous number of music-genres ID3-Tags provide we will probably choose a tree structure in addition to the text-based genre-navigator on the right in lower levels. This structure should be similar to a hyperbolic tree.

Dimension "Genre-Multitude" --> visual attribute "Tree Branches"

*To afford best visualization we are right now not sure whether we should display the percentage-rate in bars as shown in the fake-screenshot below, or should rather use a Bubble-Chart, where the size of the bubbles is determined by the values of occurrence per genre.

Dimension "File-Occurrence" --> visual attribute "Area"

=== Specification of used Techniques / applied Principles ===

*Hyperbolic Trees:
(s. slide 18, 19, 20 of Info_Vis4.pdf handed out in the course “188.305 VO Informationsvisualisierung”)

Due to the enormous number of music-genres ID-3 Tags provide we probably choose a tree structure
similar to a hyperbolic tree to visualize the raw data extracted from the corresponding mp3-archiv.

*Bubble Chart:
(s. similar to bubble-maps, slide 52 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung, http://peltiertech.com/Excel/ChartsHowTo/HowToBubble.html)

Bubble chart, where the size of the bubbles is determined by the values of occurrence.

*Details on demand:
(s. slide 55 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Used several times. (File-Explorer, Genre-Information, ...)

*Linking & Brushing:
(s. slide 100 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Detail Windows containing percentage-distribution according to the specific genre

*Focus & Context: Tiled Multi-Level Browser
(slide 69 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Overview Window, Details on Demand Window

=== Possibilities of User-Interaction ===

* Assemble information about occurence-percentage per genre
* Assemble information according to ID3-Tags
* Group genres
* Build paths
* Build new tree
* Modify leaves
* Save play-list

=== Mockup / Fake Screenshot ===

[[Image:MP3_Viewer.gif]]

== References ==

[Wikipedia, 2005a] ID3, Wikipedia, Last updated: 21 November, 2005, Retrieved at: November 22, 2005, http://www.csam.montclair.edu/~mcdougal/SCP/D_types.htm

[Wikipedia, 2005b] MP3, Wikipedia, Last updated: 21 November, 2005, Retrieved at: November 22, 2005, http://en.wikipedia.org/wiki/Mp3

[Id3.org, 2004] ID3v2 frames, Id3.org, Last updated: 28. Februar, 2004, Retrieved at: November 22, 2005, http://www.id3.org/frames.html

[Montclaire, 2000] Data Types, Department of Science and Mathematics at Montclair State University, Last updated: 3. August, 2000, Retrieved at: November 22, 2005, http://www.csam.montclair.edu/~mcdougal/SCP/D_types.htm

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 3 - Design

2005-12-02T08:41:39Z

UE-InfoVis0506 0026030: /* Are there any known / often used Methods / Visualisation Techniques? */

== Topic ==
'''MP3 Archive Visualization'''
== Specification of the Application Area and the given Dataset ==
=== Application area Analysis ===

The application area in this case is the creation of a clearly arranged visualization for a big music archive consisting of several thousand files. This can be achieved by using the values of the ID3-tags as well as the attributes of the music files itself.

Since the existing data mainly consist of discrete values, our mission to produce an appropriate visualization will basically be achieved with the creation of quantitative data by using a reasonable combination of given values.

Most Audio formats nowadays use the container format ID3 to store additional Information about the file, apart from the given file properties.

ID3 generally supports loads of different input values and to visualize them all would go beyond the scope of our task. Besides in most cases only the most common values like Album, Interpret, Year… are specified correctly.
Therefore we will concentrate our work for our prototype on these entries.

=== Dataset Analysis ===

{|
|- style="background:#FF6633"
!Attribute
!Data type
!Description
|-
|Title
|Discrete
|Name of the Song
|- style="background:#FFCC99"
|Interpret
|Discrete
|Name of the Artist/Band who plays the song
|-
|Album
|Discrete
|Name of the Album/Compilation the song belongs to
|- style="background:#FFCC99"
|Year
|Ordinal
|Year the song was recorded in
|-
|Genre
|Nominal
|Genre the song belongs to
|- style="background:#FFCC99"
|Size
|Quantitative
|Size of the file
|-
|Duration
|Quantitative
|Overall play length of the song
|- style="background:#FFCC99"
|Bit rate
|Ordinal
|Bit rate the file was encoded with
|-
|Sample rate
|Ordinal
|Sample rate the file was encoded with
|- style="background:#FFCC99"
|File Location
|Discrete
|Full system path to the file
|}

A complete data record consists of all attributes listed above.

All data in this set is one-dimensional.

== Analysis of the Target User Group ==

=== Who should use this kind of visualization technique? ===

This visualization technique is mainly meant for the 'end-users', that is someone who collects lots of MP3s. With 'lots of MP3s' we mean quite a few GBs, just more than 30 GBs. Our visualization should help the user to get an overview of his collection and his listening-habits. By using this visualization technique, the user will get informations to one chosen artist and whose discography. For example: in his database the user has got the band radiohead, who produced albums over 15 years and in this period they changed their musicstyle from alternative rock to experimental electronic. The visualization will show him from which producing period he has got more MP3s and which period he likes more, by counting the number of listenings of each song. The result could be that he has got more MP3s from their early years, but likes the experimental electronic tracks more.
This visualization technique could also be interesting for the band and the music industry, if they want to produce a new album. for example: a band (like radiohead) who changed their musicstyle over the years and wants to know which style is preferd more. But therefor they have to compare these dates from many users.

=== What are the characteristics of the target group? ===

People of this group are music enthusiasts. They have thousands of mp3s on their harddisk and love it to collect them. Most of them have lost track of their collection, on the strength of the abundance of their collection. These people mainly receive their mp3s from the internet instead of buying CDs, because they like to see their whole musiccollection at a glance.

=== Are there any known / often used Methods / Visualisation Techniques? ===

No, we don't know any similar visualization technique. ITunes only shows textbasedly how often a song was heard.

== Intended Purpose of our Visualization ==

=== What should be achieved with this visualization? ===
A better information representation of the mp3s should be achieved by combining several ID3-tags. The representation of the data and the contained information should be effective, precise and self-explanatory, so that the user can get a good and expressive overview of his collection and the contained information.

=== Which tasks should be solved? ===
The data readout of contentspecific characteristics should be possible.
We want to visualize the distribution of the genres, so that the user is able to group them how he likes. e.g.: there is a group called 'classics' and the user wants to put the group 'oldies' to them, so he can make a subgroup.

=== Questions that should be solved with this visualization technique ===
It should be possible to get the frequency distribution of the mp3s relating to their characteristics. But the precondition therefor is that the ID3-tags are available

== Proposal of Design ==

=== Types of Visualization applied ===

As there are no comparable features implemented in existing mp3 archival storage systems we decided to implement a visualization kit exploring the quantitative distribution of an mp3-archive according to the different genres. As a default the main window shows several genres the authors considered to be the right ones for music main-genres in the root directory including their percentage-rate corresponding to the whole file-archive.

By a single click on one of the main genres the user gets additional information in the upper right window on the one hand and of course a file-listing in the lower left window on the other hand. Double clicking a genre in the main window brings the user straight to a sub-level containing the corresponding sub-genres again shown in the main window. Through the path shown on top of the main window the system allows to easily jump back to wherever necessary out of every level.

Further on the user is able to read out file-specific information like Title, Interpret,
Album, Year, Duration & Bit-Rate through a click on the particular file listed below, but is also allowed to save a play-list from the files selected via the file-explorer.

Following feature turns the visualization kit into a rather complex and outstanding tool. By default the tool sorts the input data through a given hierarchy according to the main genres and alternatively to the sub-genres following the ID3-Tags. Additionally the User has the ability to change this system and to create his own view according to his point of interest. That means that he is able to change the main genres and the sub-genres as well. As there are always different views especially on the terms of sub-genres the user is even allowed to change the structure of the given categories. That means that one is permitted to extract an ambiguous sub-genre to directly attach it to another genre. This will be able via the right middle window showing the sub-genres and their actual belongings as well. Probably the system will give the possibility to cancel unmeant genres at all.

=== Visual Mapping ===

*Due to the enormous number of music-genres ID3-Tags provide we will probably choose a tree structure in addition to the text-based genre-navigator on the right in lower levels. This structure should be similar to a hyperbolic tree.

Dimension "Genre-Multitude" --> visual attribute "Tree Branches"

*To afford best visualization we are right now not sure whether we should display the percentage-rate in bars as shown in the fake-screenshot below, or should rather use a Bubble-Chart, where the size of the bubbles is determined by the values of occurrence per genre.

Dimension "File-Occurrence" --> visual attribute "Area"

=== Specification of used Techniques / applied Principles ===

*Hyperbolic Trees:
(s. slide 18, 19, 20 of Info_Vis4.pdf handed out in the course “188.305 VO Informationsvisualisierung”)

Due to the enormous number of music-genres ID-3 Tags provide we probably choose a tree structure
similar to a hyperbolic tree to visualize the raw data extracted from the corresponding mp3-archiv.

*Bubble Chart:
(s. similar to bubble-maps, slide 52 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung, http://peltiertech.com/Excel/ChartsHowTo/HowToBubble.html)

Bubble chart, where the size of the bubbles is determined by the values of occurrence.

*Details on demand:
(s. slide 55 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Used several times. (File-Explorer, Genre-Information, ...)

*Linking & Brushing:
(s. slide 100 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Detail Windows containing percentage-distribution according to the specific genre

*Focus & Context: Tiled Multi-Level Browser
(slide 69 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Overview Window, Details on Demand Window

=== Possibilities of User-Interaction ===

* Assemble information about occurence-percentage per genre
* Assemble information according to ID3-Tags
* Group genres
* Build paths
* Build new tree
* Modify leaves
* Save play-list

=== Mockup / Fake Screenshot ===

[[Image:MP3_Viewer.gif]]

== References ==

[Wikipedia, 2005a] ID3, Wikipedia, Last updated: 21 November, 2005, Retrieved at: November 22, 2005, http://www.csam.montclair.edu/~mcdougal/SCP/D_types.htm

[Wikipedia, 2005b] MP3, Wikipedia, Last updated: 21 November, 2005, Retrieved at: November 22, 2005, http://en.wikipedia.org/wiki/Mp3

[Id3.org, 2004] ID3v2 frames, Id3.org, Last updated: 28. Februar, 2004, Retrieved at: November 22, 2005, http://www.id3.org/frames.html

[Montclaire, 2000] Data Types, Department of Science and Mathematics at Montclair State University, Last updated: 3. August, 2000, Retrieved at: November 22, 2005, http://www.csam.montclair.edu/~mcdougal/SCP/D_types.htm

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 3 - Design

2005-11-22T22:42:50Z

UE-InfoVis0506 0026030: /* Which tasks should be solved? */

== Topic ==
'''MP3 Archive Visualization'''
== Specification of the Application Area and the given Dataset ==
=== Application area Analysis ===

The application area in this case is the creation of a clearly arranged visualization for a big music archive consisting of several thousand files. This can be achieved by using the values of the ID3-tags as well as the attributes of the music files itself.

Since the existing data mainly consist of discrete values, our mission to produce an appropriate visualization will basically be achieved with the creation of quantitative data by using a reasonable combination of given values.

Most Audio formats nowadays use the container format ID3 to store additional Information about the file, apart from the given file properties.

ID3 generally supports loads of different input values and to visualize them all would go beyond the scope of our task. Besides in most cases only the most common values like Album, Interpret, Year… are specified correctly.
Therefore we will concentrate our work for our prototype on these entries.

=== Dataset Analysis ===

{|
|- style="background:#FF6633"
!Attribute
!Data type
!Description
|-
|Title
|Discrete
|Name of the Song
|- style="background:#FFCC99"
|Interpret
|Discrete
|Name of the Artist/Band who plays the song
|-
|Album
|Discrete
|Name of the Album/Compilation the song belongs to
|- style="background:#FFCC99"
|Year
|Ordinal
|Year the song was recorded in
|-
|Genre
|Nominal
|Genre the song belongs to
|- style="background:#FFCC99"
|Size
|Quantitative
|Size of the file
|-
|Duration
|Quantitative
|Overall play length of the song
|- style="background:#FFCC99"
|Bit rate
|Ordinal
|Bit rate the file was encoded with
|-
|Sample rate
|Ordinal
|Sample rate the file was encoded with
|- style="background:#FFCC99"
|File Location
|Discrete
|Full system path to the file
|}

A complete data record consists of all attributes listed above.

All data in this set is one-dimensional.

== Analysis of the Target User Group ==

=== Who should use this kind of visualization technique? ===

This visualization technique is mainly meant for the 'end-users', that is someone who collects lots of mp3s. Our visualization should help the user to get an overview of his collection and discover coherences between them. The user should also get some more information about his collection which he did not know before or which he could not find in any usual representation.

=== What are the characteristics of the target group? ===

People of this group are music enthusiasts. They have thousands of mp3s on their harddisk and love it to collect them. These people mainly receive their mp3s from the internet instead of buying CDs, because they like it to see their whole musiccollection at a glance and want to browse through it in many different ways.

=== Are there any known / often used Methods / Visualisation Techniques? ===

No, until now only the textbased listing-technique is used.

== Intended Purpose of our Visualization ==

=== What should be achieved with this visualization? ===
A better information representation of the mp3s should be achieved by combining several ID3-tags. The representation of the data and the contained information should be effective, precise and self-explanatory, so that the user can get a good and expressive overview of his collection and the contained information.

=== Which tasks should be solved? ===
The data readout of contentspecific characteristics should be possible.
We want to visualize the distribution of the genres, so that the user is able to group them how he likes. e.g.: there is a group called 'classics' and the user wants to put the group 'oldies' to them, so he can make a subgroup.

=== Questions that should be solved with this visualization technique ===
It should be possible to get the frequency distribution of the mp3s relating to their characteristics. But the precondition therefor is that the ID3-tags are available

== Proposal of Design ==

=== Types of Visualization applied ===

As there are no comparable features implemented in existing mp3 archival storage systems we decided to implement a visualization kit exploring the quantitative distribution of an mp3-archive according to the different genres. As a default the main window shows several genres the authors considered to be the right ones for music main-genres in the root directory including their percentage-rate corresponding to the whole file-archive.

By a single click on one of the main genres the user gets additional information in the upper right window on the one hand and of course a file-listing in the lower left window on the other hand. Double clicking a genre in the main window brings the user straight to a sub-level containing the corresponding sub-genres again shown in the main window. Through the path shown on top of the main window the system allows to easily jump back to wherever necessary out of every level.

Further on the user is able to read out file-specific information like Title, Interpret,
Album, Year, Duration & Bit-Rate through a click on the particular file listed below, but is also allowed to save a play-list from the files selected via the file-explorer.

Following feature turns the visualization kit into a rather complex and outstanding tool. By default the tool sorts the input data through a given hierarchy according to the main genres and alternatively to the sub-genres following the ID3-Tags. Additionally the User has the ability to change this system and to create his own view according to his point of interest. That means that he is able to change the main genres and the sub-genres as well. As there are always different views especially on the terms of sub-genres the user is even allowed to change the structure of the given categories. That means that one is permitted to extract an ambiguous sub-genre to directly attach it to another genre. This will be able via the right middle window showing the sub-genres and their actual belongings as well. Probably the system will give the possibility to cancel unmeant genres at all.

=== Visual Mapping ===

*Due to the enormous number of music-genres ID3-Tags provide we will probably choose a tree structure in addition to the text-based genre-navigator on the right in lower levels. This structure should be similar to a hyperbolic tree.

Dimension "Genre-Multitude" --> visual attribute "Tree Branches"

*To afford best visualization we are right now not sure whether we should display the percentage-rate in bars as shown in the fake-screenshot below, or should rather use a Bubble-Chart, where the size of the bubbles is determined by the values of occurrence per genre.

Dimension "File-Occurrence" --> visual attribute "Area"

=== Specification of used Techniques / applied Principles ===

*Hyperbolic Trees:
(s. slide 18, 19, 20 of Info_Vis4.pdf handed out in the course “188.305 VO Informationsvisualisierung”)

Due to the enormous number of music-genres ID-3 Tags provide we probably choose a tree structure
similar to a hyperbolic tree to visualize the raw data extracted from the corresponding mp3-archiv.

*Bubble Chart:
(s. similar to bubble-maps, slide 52 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung, http://peltiertech.com/Excel/ChartsHowTo/HowToBubble.html)

Bubble chart, where the size of the bubbles is determined by the values of occurrence.

*Details on demand:
(s. slide 55 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Used several times. (File-Explorer, Genre-Information, ...)

*Linking & Brushing:
(s. slide 100 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Detail Windows containing percentage-distribution according to the specific genre

*Focus & Context: Tiled Multi-Level Browser
(slide 69 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Overview Window, Details on Demand Window

=== Possibilities of User-Interaction ===

* Assemble information about occurence-percentage per genre
* Assemble information according to ID3-Tags
* Group genres
* Build paths
* Build new tree
* Modify leaves
* Save play-list

=== Mockup / Fake Screenshot ===

[[Image:MP3_Viewer.gif]]

== References ==

[Wikipedia, 2005a] ID3, Wikipedia, Last updated: 21 November, 2005, Retrieved at: November 22, 2005, http://www.csam.montclair.edu/~mcdougal/SCP/D_types.htm

[Wikipedia, 2005b] MP3, Wikipedia, Last updated: 21 November, 2005, Retrieved at: November 22, 2005, http://en.wikipedia.org/wiki/Mp3

[Id3.org, 2004] ID3v2 frames, Id3.org, Last updated: 28. Februar, 2004, Retrieved at: November 22, 2005, http://www.id3.org/frames.html

[Montclaire, 2000] Data Types, Department of Science and Mathematics at Montclair State University, Last updated: 3. August, 2000, Retrieved at: November 22, 2005, http://www.csam.montclair.edu/~mcdougal/SCP/D_types.htm

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 3 - Design

2005-11-22T21:57:02Z

UE-InfoVis0506 0026030: /* What should be achieved with this visualization? */

== Topic ==
'''MP3 Archive Visualization'''
== Specification of the Application Area and the given Dataset ==
=== Application area Analysis ===

The application area in this case is the creation of a clearly arranged visualization for a big music archive consisting of several thousand files. This can be achieved by using the values of the ID3-tags as well as the attributes of the music files itself.

Since the existing data mainly consist of discrete values, our mission to produce an appropriate visualization will basically be achieved with the creation of quantitative data by using a reasonable combination of given values.

Most Audio formats nowadays use the container format ID3 to store additional Information about the file, apart from the given file properties.

ID3 generally supports loads of different input values and to visualize them all would go beyond the scope of our task. Besides in most cases only the most common values like Album, Interpret, Year… are specified correctly.
Therefore we will concentrate our work for our prototype on these entries.

=== Dataset Analysis ===

{|
|- style="background:#FF6633"
!Attribute
!Data type
!Description
|-
|Title
|Discrete
|Name of the Song
|- style="background:#FFCC99"
|Interpret
|Discrete
|Name of the Artist/Band who plays the song
|-
|Album
|Discrete
|Name of the Album/Compilation the song belongs to
|- style="background:#FFCC99"
|Year
|Ordinal
|Year the song was recorded in
|-
|Genre
|Nominal
|Genre the song belongs to
|- style="background:#FFCC99"
|Size
|Quantitative
|Size of the file
|-
|Duration
|Quantitative
|Overall play length of the song
|- style="background:#FFCC99"
|Bit rate
|Ordinal
|Bit rate the file was encoded with
|-
|Sample rate
|Ordinal
|Sample rate the file was encoded with
|- style="background:#FFCC99"
|File Location
|Discrete
|Full system path to the file
|}

A complete data record consists of all attributes listed above.

All data in this set is one-dimensional.

== Analysis of the Target User Group ==

=== Who should use this kind of visualization technique? ===

This visualization technique is mainly meant for the 'end-users', that is someone who collects lots of mp3s. Our visualization should help the user to get an overview of his collection and discover coherences between them. The user should also get some more information about his collection which he did not know before or which he could not find in any usual representation.

=== What are the characteristics of the target group? ===

People of this group are music enthusiasts. They have thousands of mp3s on their harddisk and love it to collect them. These people mainly receive their mp3s from the internet instead of buying CDs, because they like it to see their whole musiccollection at a glance and want to browse through it in many different ways.

=== Are there any known / often used Methods / Visualisation Techniques? ===

No, until now only the textbased listing-technique is used.

== Intended Purpose of our Visualization ==

=== What should be achieved with this visualization? ===
A better information representation of the mp3s should be achieved by combining several ID3-tags. The representation of the data and the contained information should be effective, precise and self-explanatory, so that the user can get a good and expressive overview of his collection and the contained information.

=== Which tasks should be solved? ===
The data readout of contentspecific characteristics should be possible.
These characteristics could be e.g.:
* 'the longest song' of the mp3-collection or
* 'the most present genre' or
* 'the most often heard song' or
* 'the Top Ten'
* etc.

=== Questions that should be solved with this visualization technique ===
It should be possible to get the frequency distribution of the mp3s relating to their characteristics. But the precondition therefor is that the ID3-tags are available

== Proposal of Design ==

=== Types of Visualization applied ===

As there are no comparable features implemented in existing mp3 archival storage systems we decided to implement a visualization kit exploring the quantitative distribution of an mp3-archive according to the different genres. As a default the main window shows several genres the authors considered to be the right ones for music main-genres in the root directory including their percentage-rate corresponding to the whole file-archive.

By a single click on one of the main genres the user gets additional information in the upper right window on the one hand and of course a file-listing in the lower left window on the other hand. Double clicking a genre in the main window brings the user straight to a sub-level containing the corresponding sub-genres again shown in the main window. Through the path shown on top of the main window the system allows to easily jump back to wherever necessary out of every level.

Further on the user is able to read out file-specific information like Title, Interpret,
Album, Year, Duration & Bit-Rate through a click on the particular file listed below, but is also allowed to save a play-list from the files selected via the file-explorer.

Following feature turns the visualization kit into a rather complex and outstanding tool. By default the tool sorts the input data through a given hierarchy according to the main genres and alternatively to the sub-genres following the ID3-Tags. Additionally the User has the ability to change this system and to create his own view according to his point of interest. That means that he is able to change the main genres and the sub-genres as well. As there are always different views especially on the terms of sub-genres the user is even allowed to change the structure of the given categories. That means that one is permitted to extract an ambiguous sub-genre to directly attach it to another genre. This will be able via the right middle window showing the sub-genres and their actual belongings as well. Probably the system will give the possibility to cancel unmeant genres at all.

=== Visual Mapping ===

*Due to the enormous number of music-genres ID3-Tags provide we will probably choose a tree structure in addition to the text-based genre-navigator on the right in lower levels. This structure should be similar to a hyperbolic tree.

Dimension "Genre-Multitude" --> visual attribute "Tree Branches"

*To afford best visualization we are right now not sure whether we should display the percentage-rate in bars as shown in the fake-screenshot below, or should rather use a Bubble-Chart, where the size of the bubbles is determined by the values of occurrence per genre.

Dimension "File-Occurrence" --> visual attribute "Area"

=== Specification of used Techniques / applied Principles ===

*Hyperbolic Trees:
(s. slide 18, 19, 20 of Info_Vis4.pdf handed out in the course “188.305 VO Informationsvisualisierung”)

Due to the enormous number of music-genres ID-3 Tags provide we probably choose a tree structure
similar to a hyperbolic tree to visualize the raw data extracted from the corresponding mp3-archiv.

*Bubble Chart:
(s. similar to bubble-maps, slide 52 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung, http://peltiertech.com/Excel/ChartsHowTo/HowToBubble.html)

Bubble chart, where the size of the bubbles is determined by the values of occurrence.

*Details on demand:
(s. slide 55 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Used several times. (File-Explorer, Genre-Information, ...)

*Linking & Brushing:
(s. slide 100 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Detail Windows containing percentage-distribution according to the specific genre

*Focus & Context: Tiled Multi-Level Browser
(slide 69 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Overview Window, Details on Demand Window

=== Possibilities of User-Interaction ===

* Assemble information about occurence-percentage per genre
* Assemble information according to ID3-Tags
* Group genres
* Build paths
* Build new tree
* Modify leaves
* Save play-list

=== Mockup / Fake Screenshot ===

[[Image:MP3_Viewer.gif]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 3 - Design

2005-11-22T21:56:05Z

UE-InfoVis0506 0026030: /* What are the characteristics of the target group? */

== Topic ==
'''MP3 Archive Visualization'''
== Specification of the Application Area and the given Dataset ==
=== Application area Analysis ===

The application area in this case is the creation of a clearly arranged visualization for a big music archive consisting of several thousand files. This can be achieved by using the values of the ID3-tags as well as the attributes of the music files itself.

Since the existing data mainly consist of discrete values, our mission to produce an appropriate visualization will basically be achieved with the creation of quantitative data by using a reasonable combination of given values.

Most Audio formats nowadays use the container format ID3 to store additional Information about the file, apart from the given file properties.

ID3 generally supports loads of different input values and to visualize them all would go beyond the scope of our task. Besides in most cases only the most common values like Album, Interpret, Year… are specified correctly.
Therefore we will concentrate our work for our prototype on these entries.

=== Dataset Analysis ===

{|
|- style="background:#FF6633"
!Attribute
!Data type
!Description
|-
|Title
|Discrete
|Name of the Song
|- style="background:#FFCC99"
|Interpret
|Discrete
|Name of the Artist/Band who plays the song
|-
|Album
|Discrete
|Name of the Album/Compilation the song belongs to
|- style="background:#FFCC99"
|Year
|Ordinal
|Year the song was recorded in
|-
|Genre
|Nominal
|Genre the song belongs to
|- style="background:#FFCC99"
|Size
|Quantitative
|Size of the file
|-
|Duration
|Quantitative
|Overall play length of the song
|- style="background:#FFCC99"
|Bit rate
|Ordinal
|Bit rate the file was encoded with
|-
|Sample rate
|Ordinal
|Sample rate the file was encoded with
|- style="background:#FFCC99"
|File Location
|Discrete
|Full system path to the file
|}

A complete data record consists of all attributes listed above.

All data in this set is one-dimensional.

== Analysis of the Target User Group ==

=== Who should use this kind of visualization technique? ===

This visualization technique is mainly meant for the 'end-users', that is someone who collects lots of mp3s. Our visualization should help the user to get an overview of his collection and discover coherences between them. The user should also get some more information about his collection which he did not know before or which he could not find in any usual representation.

=== What are the characteristics of the target group? ===

People of this group are music enthusiasts. They have thousands of mp3s on their harddisk and love it to collect them. These people mainly receive their mp3s from the internet instead of buying CDs, because they like it to see their whole musiccollection at a glance and want to browse through it in many different ways.

=== Are there any known / often used Methods / Visualisation Techniques? ===

No, until now only the textbased listing-technique is used.

== Intended Purpose of our Visualization ==

=== What should be achieved with this visualization? ===
It should be achieved a better information representation of the mp3s by combining several ID3-tags. The representation of the data and the contained information should be effective, precise and self-explanatory, so that the user can get a good and expressive overview of his collection and the contained information.

=== Which tasks should be solved? ===
The data readout of contentspecific characteristics should be possible.
These characteristics could be e.g.:
* 'the longest song' of the mp3-collection or
* 'the most present genre' or
* 'the most often heard song' or
* 'the Top Ten'
* etc.

=== Questions that should be solved with this visualization technique ===
It should be possible to get the frequency distribution of the mp3s relating to their characteristics. But the precondition therefor is that the ID3-tags are available

== Proposal of Design ==

=== Types of Visualization applied ===

As there are no comparable features implemented in existing mp3 archival storage systems we decided to implement a visualization kit exploring the quantitative distribution of an mp3-archive according to the different genres. As a default the main window shows several genres the authors considered to be the right ones for music main-genres in the root directory including their percentage-rate corresponding to the whole file-archive.

By a single click on one of the main genres the user gets additional information in the upper right window on the one hand and of course a file-listing in the lower left window on the other hand. Double clicking a genre in the main window brings the user straight to a sub-level containing the corresponding sub-genres again shown in the main window. Through the path shown on top of the main window the system allows to easily jump back to wherever necessary out of every level.

Further on the user is able to read out file-specific information like Title, Interpret,
Album, Year, Duration & Bit-Rate through a click on the particular file listed below, but is also allowed to save a play-list from the files selected via the file-explorer.

Following feature turns the visualization kit into a rather complex and outstanding tool. By default the tool sorts the input data through a given hierarchy according to the main genres and alternatively to the sub-genres following the ID3-Tags. Additionally the User has the ability to change this system and to create his own view according to his point of interest. That means that he is able to change the main genres and the sub-genres as well. As there are always different views especially on the terms of sub-genres the user is even allowed to change the structure of the given categories. That means that one is permitted to extract an ambiguous sub-genre to directly attach it to another genre. This will be able via the right middle window showing the sub-genres and their actual belongings as well. Probably the system will give the possibility to cancel unmeant genres at all.

=== Visual Mapping ===

*Due to the enormous number of music-genres ID3-Tags provide we will probably choose a tree structure in addition to the text-based genre-navigator on the right in lower levels. This structure should be similar to a hyperbolic tree.

Dimension "Genre-Multitude" --> visual attribute "Tree Branches"

*To afford best visualization we are right now not sure whether we should display the percentage-rate in bars as shown in the fake-screenshot below, or should rather use a Bubble-Chart, where the size of the bubbles is determined by the values of occurrence per genre.

Dimension "File-Occurrence" --> visual attribute "Area"

=== Specification of used Techniques / applied Principles ===

*Hyperbolic Trees:
(s. slide 18, 19, 20 of Info_Vis4.pdf handed out in the course “188.305 VO Informationsvisualisierung”)

Due to the enormous number of music-genres ID-3 Tags provide we probably choose a tree structure
similar to a hyperbolic tree to visualize the raw data extracted from the corresponding mp3-archiv.

*Bubble Chart:
(s. similar to bubble-maps, slide 52 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung, http://peltiertech.com/Excel/ChartsHowTo/HowToBubble.html)

Bubble chart, where the size of the bubbles is determined by the values of occurrence.

*Details on demand:
(s. slide 55 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Used several times. (File-Explorer, Genre-Information, ...)

*Linking & Brushing:
(s. slide 100 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Detail Windows containing percentage-distribution according to the specific genre

*Focus & Context: Tiled Multi-Level Browser
(slide 69 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Overview Window, Details on Demand Window

=== Possibilities of User-Interaction ===

* Assemble information about occurence-percentage per genre
* Assemble information according to ID3-Tags
* Group genres
* Build paths
* Build new tree
* Modify leaves
* Save play-list

=== Mockup / Fake Screenshot ===

[[Image:MP3_Viewer.gif]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 3 - Design

2005-11-22T21:54:30Z

UE-InfoVis0506 0026030: /* Who should use this kind of visualization technique? */

== Topic ==
'''MP3 Archive Visualization'''
== Specification of the Application Area and the given Dataset ==
=== Application area Analysis ===

The application area in this case is the creation of a clearly arranged visualization for a big music archive consisting of several thousand files. This can be achieved by using the values of the ID3-tags as well as the attributes of the music files itself.

Since the existing data mainly consist of discrete values, our mission to produce an appropriate visualization will basically be achieved with the creation of quantitative data by using a reasonable combination of given values.

Most Audio formats nowadays use the container format ID3 to store additional Information about the file, apart from the given file properties.

ID3 generally supports loads of different input values and to visualize them all would go beyond the scope of our task. Besides in most cases only the most common values like Album, Interpret, Year… are specified correctly.
Therefore we will concentrate our work for our prototype on these entries.

=== Dataset Analysis ===

{|
|- style="background:#FF6633"
!Attribute
!Data type
!Description
|-
|Title
|Discrete
|Name of the Song
|- style="background:#FFCC99"
|Interpret
|Discrete
|Name of the Artist/Band who plays the song
|-
|Album
|Discrete
|Name of the Album/Compilation the song belongs to
|- style="background:#FFCC99"
|Year
|Ordinal
|Year the song was recorded in
|-
|Genre
|Nominal
|Genre the song belongs to
|- style="background:#FFCC99"
|Size
|Quantitative
|Size of the file
|-
|Duration
|Quantitative
|Overall play length of the song
|- style="background:#FFCC99"
|Bit rate
|Ordinal
|Bit rate the file was encoded with
|-
|Sample rate
|Ordinal
|Sample rate the file was encoded with
|- style="background:#FFCC99"
|File Location
|Discrete
|Full system path to the file
|}

A complete data record consists of all attributes listed above.

All data in this set is one-dimensional.

== Analysis of the Target User Group ==

=== Who should use this kind of visualization technique? ===

This visualization technique is mainly meant for the 'end-users', that is someone who collects lots of mp3s. Our visualization should help the user to get an overview of his collection and discover coherences between them. The user should also get some more information about his collection which he did not know before or which he could not find in any usual representation.

=== What are the characteristics of the target group? ===

People of this group are music enthusiasts. They have thousands of mp3s on their harddisk, and love it to collect mp3s. These people mainly receive their mp3s from the internet instead of buying CDs, because they like to see their whole collection at a glance and want to browse through it in many different ways.

=== Are there any known / often used Methods / Visualisation Techniques? ===

No, until now only the textbased listing-technique is used.

== Intended Purpose of our Visualization ==

=== What should be achieved with this visualization? ===
It should be achieved a better information representation of the mp3s by combining several ID3-tags. The representation of the data and the contained information should be effective, precise and self-explanatory, so that the user can get a good and expressive overview of his collection and the contained information.

=== Which tasks should be solved? ===
The data readout of contentspecific characteristics should be possible.
These characteristics could be e.g.:
* 'the longest song' of the mp3-collection or
* 'the most present genre' or
* 'the most often heard song' or
* 'the Top Ten'
* etc.

=== Questions that should be solved with this visualization technique ===
It should be possible to get the frequency distribution of the mp3s relating to their characteristics. But the precondition therefor is that the ID3-tags are available

== Proposal of Design ==

=== Types of Visualization applied ===

As there are no comparable features implemented in existing mp3 archival storage systems we decided to implement a visualization kit exploring the quantitative distribution of an mp3-archive according to the different genres. As a default the main window shows several genres the authors considered to be the right ones for music main-genres in the root directory including their percentage-rate corresponding to the whole file-archive.

By a single click on one of the main genres the user gets additional information in the upper right window on the one hand and of course a file-listing in the lower left window on the other hand. Double clicking a genre in the main window brings the user straight to a sub-level containing the corresponding sub-genres again shown in the main window. Through the path shown on top of the main window the system allows to easily jump back to wherever necessary out of every level.

Further on the user is able to read out file-specific information like Title, Interpret,
Album, Year, Duration & Bit-Rate through a click on the particular file listed below, but is also allowed to save a play-list from the files selected via the file-explorer.

Following feature turns the visualization kit into a rather complex and outstanding tool. By default the tool sorts the input data through a given hierarchy according to the main genres and alternatively to the sub-genres following the ID3-Tags. Additionally the User has the ability to change this system and to create his own view according to his point of interest. That means that he is able to change the main genres and the sub-genres as well. As there are always different views especially on the terms of sub-genres the user is even allowed to change the structure of the given categories. That means that one is permitted to extract an ambiguous sub-genre to directly attach it to another genre. This will be able via the right middle window showing the sub-genres and their actual belongings as well. Probably the system will give the possibility to cancel unmeant genres at all.

=== Visual Mapping ===

*Due to the enormous number of music-genres ID3-Tags provide we will probably choose a tree structure in addition to the text-based genre-navigator on the right in lower levels. This structure should be similar to a hyperbolic tree.

Dimension "Genre-Multitude" --> visual attribute "Tree Branches"

*To afford best visualization we are right now not sure whether we should display the percentage-rate in bars as shown in the fake-screenshot below, or should rather use a Bubble-Chart, where the size of the bubbles is determined by the values of occurrence per genre.

Dimension "File-Occurrence" --> visual attribute "Area"

=== Specification of used Techniques / applied Principles ===

*Hyperbolic Trees:
(s. slide 18, 19, 20 of Info_Vis4.pdf handed out in the course “188.305 VO Informationsvisualisierung”)

Due to the enormous number of music-genres ID-3 Tags provide we probably choose a tree structure
similar to a hyperbolic tree to visualize the raw data extracted from the corresponding mp3-archiv.

*Bubble Chart:
(s. similar to bubble-maps, slide 52 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung, http://peltiertech.com/Excel/ChartsHowTo/HowToBubble.html)

Bubble chart, where the size of the bubbles is determined by the values of occurrence.

*Details on demand:
(s. slide 55 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Used several times. (File-Explorer, Genre-Information, ...)

*Linking & Brushing:
(s. slide 100 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Detail Windows containing percentage-distribution according to the specific genre

*Focus & Context: Tiled Multi-Level Browser
(slide 69 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Overview Window, Details on Demand Window

=== Possibilities of User-Interaction ===

* Assemble information about occurence-percentage per genre
* Assemble information according to ID3-Tags
* Group genres
* Build paths
* Build new tree
* Modify leaves
* Save play-list

=== Mockup / Fake Screenshot ===

[[Image:MP3_Viewer.gif]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 3 - Design

2005-11-22T17:37:46Z

UE-InfoVis0506 0026030: /* Which tasks should be solved? */

== Specification of the Application Area and the given Dataset ==
== Analysis of the Target User Group ==

=== Who should use this kind of visualization technique? ===

This visualization technique is mainly meant for the 'end-users', that is someone who collects lots of mp3s. Our visualization should help the user to get an overview of his collection and discover coherences between . The user should also get some more information about his collection, that he did not know before or that he could not find in any usual representation.

=== What are the characteristics of the target group? ===

People of this group are musicenthusiasts. They have more than thousands of mp3s on their harddisk, and love it to collect mp3s. This people mainly receive their mp3s from the internet instead of buying CDs, because they like to see their whole collection at a glance and to browse through it in many different ways.

=== Are there any known / often used Methods / Visualisation Techniques? ===

No, until know only the textbased listing-technique is used.

== Intended Purpose of our Visualization ==

=== What should be achieved with this visualization? ===
It should be achieved a better information representation of the mp3s by combining several ID3-tags. The representation of the data and the contained information should be effective, precise and self-explanatory, so that the user can get a good and expressive overview of his collection and the contained information.

=== Which tasks should be solved? ===
The data readout of contentspecific characteristics should be possible.
These characteristics could be e.g.:
* 'the longest song' of the mp3-collection or
* 'the most present genre' or
* 'the most often heard song' or
* 'the Top Ten'
* etc.

=== Questions that should be solved with this visualization technique ===
It should be possible to get the frequency distribution of the mp3s relating to their characteristics. But the precondition therefor is that the ID3-tags are available

== Proposal of Design ==

=== Types of Visualization applied ===

As there are no comparable features implemented in existing mp3 archival storage systems we decided to implement a visualization kit exploring the quantitative distribution of an mp3-archive according to the different genres. As a default the main window shows several genres the authors considered to be the right ones for music main-genres in the root directory including their percentage-rate corresponding to the whole file-archive.

By a single click on one of the main genres the user gets additional information in the upper right window on the one hand and of course a file-listing in the lower left window on the other hand. Double clicking a genre in the main window brings the user straight to a sub-level containing the corresponding sub-genres again shown in the main window. Through the path shown on top of the main window the system allows to easily jump back to wherever necessary out of every level.

Further on the user is able to read out file-specific information like Title, Interpret,
Album, Year, Duration & Bit-Rate through a click on the particular file listed below, but is also allowed to save a play-list from the files selected via the file-explorer.

Following feature turns the visualization kit into a rather complex and outstanding tool. By default the tool sorts the input data through a given hierarchy according to the main genres and alternatively to the sub-genres following the ID3-Tags. Additionally the User has the ability to change this system and to create his own view according to his point of interest. That means that he is able to change the main genres and the sub-genres as well. As there are always different views especially on the terms of sub-genres the user is even allowed to change the structure of the given categories. That means that one is permitted to extract an ambiguous sub-genre to directly attach it to another genre. This will be able via the right middle window showing the sub-genres and their actual belongings as well. Probably the system will give the possibility to cancel unmeant genres at all.

=== Visual Mapping ===

*Due to the enormous number of music-genres ID3-Tags provide we will probably choose a tree structure in addition to the text-based genre-navigator on the right in lower levels. This structure should be similar to a hyperbolic tree.

Dimension "Genre-Multitude" --> visual attribute "Tree Branches"

*To afford best visualization we are right now not sure whether we should display the percentage-rate in bars as shown in the fake-screenshot below, or should rather use a Bubble-Chart, where the size of the bubbles is determined by the values of occurrence per genre.

Dimension "File-Occurrence" --> visual attribute "Area"

=== Specification of used Techniques / applied Principles ===

*Hyperbolic Trees:
(s. slide 18, 19, 20 of Info_Vis4.pdf handed out in the course “188.305 VO Informationsvisualisierung”)

Due to the enormous number of music-genres ID-3 Tags provide we probably choose a tree structure
similar to a hyperbolic tree to visualize the raw data extracted from the corresponding mp3-archiv.

*Bubble Chart:
(s. similar to bubble-maps, slide 52 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung, http://peltiertech.com/Excel/ChartsHowTo/HowToBubble.html)

Bubble chart, where the size of the bubbles is determined by the values of occurrence.

*Details on demand:
(s. slide 55 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Used several times. (File-Explorer, Genre-Information, ...)

*Linking & Brushing:
(s. slide 100 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Detail Windows containing percentage-distribution according to the specific genre

*Focus & Context: Tiled Multi-Level Browser
(slide 69 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Overview Window, Details on Demand Window

=== Possibilities of User-Interaction ===

* Assemble information about occurence-percentage per genre
* Assemble information according to ID3-Tags
* Group genres
* Build paths
* Build new tree
* Modify leaves
* Save play-list

=== Mockup / Fake Screenshot ===

[[Image:MP3_Viewer.gif]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 3 - Design

2005-11-22T17:37:32Z

UE-InfoVis0506 0026030: /* Which tasks should be solved? */

== Specification of the Application Area and the given Dataset ==
== Analysis of the Target User Group ==

=== Who should use this kind of visualization technique? ===

This visualization technique is mainly meant for the 'end-users', that is someone who collects lots of mp3s. Our visualization should help the user to get an overview of his collection and discover coherences between . The user should also get some more information about his collection, that he did not know before or that he could not find in any usual representation.

=== What are the characteristics of the target group? ===

People of this group are musicenthusiasts. They have more than thousands of mp3s on their harddisk, and love it to collect mp3s. This people mainly receive their mp3s from the internet instead of buying CDs, because they like to see their whole collection at a glance and to browse through it in many different ways.

=== Are there any known / often used Methods / Visualisation Techniques? ===

No, until know only the textbased listing-technique is used.

== Intended Purpose of our Visualization ==

=== What should be achieved with this visualization? ===
It should be achieved a better information representation of the mp3s by combining several ID3-tags. The representation of the data and the contained information should be effective, precise and self-explanatory, so that the user can get a good and expressive overview of his collection and the contained information.

=== Which tasks should be solved? ===
The data readout of contentspecific characteristics should be possible.
These characteristics could be e.g.:
* 'the longest song' of the mp3-collection or
* 'the most present genre' or
* 'the most often heard song' or
* 'the Top Ten' etc.

=== Questions that should be solved with this visualization technique ===
It should be possible to get the frequency distribution of the mp3s relating to their characteristics. But the precondition therefor is that the ID3-tags are available

== Proposal of Design ==

=== Types of Visualization applied ===

As there are no comparable features implemented in existing mp3 archival storage systems we decided to implement a visualization kit exploring the quantitative distribution of an mp3-archive according to the different genres. As a default the main window shows several genres the authors considered to be the right ones for music main-genres in the root directory including their percentage-rate corresponding to the whole file-archive.

By a single click on one of the main genres the user gets additional information in the upper right window on the one hand and of course a file-listing in the lower left window on the other hand. Double clicking a genre in the main window brings the user straight to a sub-level containing the corresponding sub-genres again shown in the main window. Through the path shown on top of the main window the system allows to easily jump back to wherever necessary out of every level.

Further on the user is able to read out file-specific information like Title, Interpret,
Album, Year, Duration & Bit-Rate through a click on the particular file listed below, but is also allowed to save a play-list from the files selected via the file-explorer.

Following feature turns the visualization kit into a rather complex and outstanding tool. By default the tool sorts the input data through a given hierarchy according to the main genres and alternatively to the sub-genres following the ID3-Tags. Additionally the User has the ability to change this system and to create his own view according to his point of interest. That means that he is able to change the main genres and the sub-genres as well. As there are always different views especially on the terms of sub-genres the user is even allowed to change the structure of the given categories. That means that one is permitted to extract an ambiguous sub-genre to directly attach it to another genre. This will be able via the right middle window showing the sub-genres and their actual belongings as well. Probably the system will give the possibility to cancel unmeant genres at all.

=== Visual Mapping ===

*Due to the enormous number of music-genres ID3-Tags provide we will probably choose a tree structure in addition to the text-based genre-navigator on the right in lower levels. This structure should be similar to a hyperbolic tree.

Dimension "Genre-Multitude" --> visual attribute "Tree Branches"

*To afford best visualization we are right now not sure whether we should display the percentage-rate in bars as shown in the fake-screenshot below, or should rather use a Bubble-Chart, where the size of the bubbles is determined by the values of occurrence per genre.

Dimension "File-Occurrence" --> visual attribute "Area"

=== Specification of used Techniques / applied Principles ===

*Hyperbolic Trees:
(s. slide 18, 19, 20 of Info_Vis4.pdf handed out in the course “188.305 VO Informationsvisualisierung”)

Due to the enormous number of music-genres ID-3 Tags provide we probably choose a tree structure
similar to a hyperbolic tree to visualize the raw data extracted from the corresponding mp3-archiv.

*Bubble Chart:
(s. similar to bubble-maps, slide 52 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung, http://peltiertech.com/Excel/ChartsHowTo/HowToBubble.html)

Bubble chart, where the size of the bubbles is determined by the values of occurrence.

*Details on demand:
(s. slide 55 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Used several times. (File-Explorer, Genre-Information, ...)

*Linking & Brushing:
(s. slide 100 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Detail Windows containing percentage-distribution according to the specific genre

*Focus & Context: Tiled Multi-Level Browser
(slide 69 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Overview Window, Details on Demand Window

=== Possibilities of User-Interaction ===

* Assemble information about occurence-percentage per genre
* Assemble information according to ID3-Tags
* Group genres
* Build paths
* Build new tree
* Modify leaves
* Save play-list

=== Mockup / Fake Screenshot ===

[[Image:MP3_Viewer.gif]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 3 - Design

2005-11-22T17:34:35Z

UE-InfoVis0506 0026030: /* Which tasks should be solved? */

== Specification of the Application Area and the given Dataset ==
== Analysis of the Target User Group ==

=== Who should use this kind of visualization technique? ===

This visualization technique is mainly meant for the 'end-users', that is someone who collects lots of mp3s. Our visualization should help the user to get an overview of his collection and discover coherences between . The user should also get some more information about his collection, that he did not know before or that he could not find in any usual representation.

=== What are the characteristics of the target group? ===

People of this group are musicenthusiasts. They have more than thousands of mp3s on their harddisk, and love it to collect mp3s. This people mainly receive their mp3s from the internet instead of buying CDs, because they like to see their whole collection at a glance and to browse through it in many different ways.

=== Are there any known / often used Methods / Visualisation Techniques? ===

No, until know only the textbased listing-technique is used.

== Intended Purpose of our Visualization ==

=== What should be achieved with this visualization? ===
It should be achieved a better information representation of the mp3s by combining several ID3-tags. The representation of the data and the contained information should be effective, precise and self-explanatory, so that the user can get a good and expressive overview of his collection and the contained information.

=== Which tasks should be solved? ===
The data readout of contentspecific characteristics should be possible.
These characteristics could be e.g.:
* 'the longest song' of the mp3-collection or
* 'the most present genre' or
* 'the most often heard song' or
* 'the Top Ten' etc.

== Proposal of Design ==

=== Types of Visualization applied ===

As there are no comparable features implemented in existing mp3 archival storage systems we decided to implement a visualization kit exploring the quantitative distribution of an mp3-archive according to the different genres. As a default the main window shows several genres the authors considered to be the right ones for music main-genres in the root directory including their percentage-rate corresponding to the whole file-archive.

By a single click on one of the main genres the user gets additional information in the upper right window on the one hand and of course a file-listing in the lower left window on the other hand. Double clicking a genre in the main window brings the user straight to a sub-level containing the corresponding sub-genres again shown in the main window. Through the path shown on top of the main window the system allows to easily jump back to wherever necessary out of every level.

Further on the user is able to read out file-specific information like Title, Interpret,
Album, Year, Duration & Bit-Rate through a click on the particular file listed below, but is also allowed to save a play-list from the files selected via the file-explorer.

Following feature turns the visualization kit into a rather complex and outstanding tool. By default the tool sorts the input data through a given hierarchy according to the main genres and alternatively to the sub-genres following the ID3-Tags. Additionally the User has the ability to change this system and to create his own view according to his point of interest. That means that he is able to change the main genres and the sub-genres as well. As there are always different views especially on the terms of sub-genres the user is even allowed to change the structure of the given categories. That means that one is permitted to extract an ambiguous sub-genre to directly attach it to another genre. This will be able via the right middle window showing the sub-genres and their actual belongings as well. Probably the system will give the possibility to cancel unmeant genres at all.

=== Visual Mapping ===

*Due to the enormous number of music-genres ID3-Tags provide we will probably choose a tree structure in addition to the text-based genre-navigator on the right in lower levels. This structure should be similar to a hyperbolic tree.

Dimension "Genre-Multitude" --> visual attribute "Tree Branches"

*To afford best visualization we are right now not sure whether we should display the percentage-rate in bars as shown in the fake-screenshot below, or should rather use a Bubble-Chart, where the size of the bubbles is determined by the values of occurrence per genre.

Dimension "File-Occurrence" --> visual attribute "Area"

=== Specification of used Techniques / applied Principles ===

*Hyperbolic Trees:
(s. slide 18, 19, 20 of Info_Vis4.pdf handed out in the course “188.305 VO Informationsvisualisierung”)

Due to the enormous number of music-genres ID-3 Tags provide we probably choose a tree structure
similar to a hyperbolic tree to visualize the raw data extracted from the corresponding mp3-archiv.

*Bubble Chart:
(s. similar to bubble-maps, slide 52 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung, http://peltiertech.com/Excel/ChartsHowTo/HowToBubble.html)

Bubble chart, where the size of the bubbles is determined by the values of occurrence.

*Details on demand:
(s. slide 55 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Used several times. (File-Explorer, Genre-Information, ...)

*Linking & Brushing:
(s. slide 100 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Detail Windows containing percentage-distribution according to the specific genre

*Focus & Context: Tiled Multi-Level Browser
(slide 69 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Overview Window, Details on Demand Window

=== Possibilities of User-Interaction ===

* Assemble information about occurence-percentage per genre
* Assemble information according to ID3-Tags
* Group genres
* Build paths
* Build new tree
* Modify leaves
* Save play-list

=== Mockup / Fake Screenshot ===

[[Image:MP3_Viewer.gif]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 3 - Design

2005-11-22T17:33:09Z

UE-InfoVis0506 0026030: /* What should be achieved with this visualization? */

== Specification of the Application Area and the given Dataset ==
== Analysis of the Target User Group ==

=== Who should use this kind of visualization technique? ===

This visualization technique is mainly meant for the 'end-users', that is someone who collects lots of mp3s. Our visualization should help the user to get an overview of his collection and discover coherences between . The user should also get some more information about his collection, that he did not know before or that he could not find in any usual representation.

=== What are the characteristics of the target group? ===

People of this group are musicenthusiasts. They have more than thousands of mp3s on their harddisk, and love it to collect mp3s. This people mainly receive their mp3s from the internet instead of buying CDs, because they like to see their whole collection at a glance and to browse through it in many different ways.

=== Are there any known / often used Methods / Visualisation Techniques? ===

No, until know only the textbased listing-technique is used.

== Intended Purpose of our Visualization ==

=== What should be achieved with this visualization? ===
It should be achieved a better information representation of the mp3s by combining several ID3-tags. The representation of the data and the contained information should be effective, precise and self-explanatory, so that the user can get a good and expressive overview of his collection and the contained information.

=== Which tasks should be solved? ===
The data readout of contentspecific characteristics should be possible. These characteristics could be e.g. 'the longest song' of the mp3-collection, or 'the most present genre', or 'the most often heard song' or the 'Top Ten' etc.

== Proposal of Design ==

=== Types of Visualization applied ===

As there are no comparable features implemented in existing mp3 archival storage systems we decided to implement a visualization kit exploring the quantitative distribution of an mp3-archive according to the different genres. As a default the main window shows several genres the authors considered to be the right ones for music main-genres in the root directory including their percentage-rate corresponding to the whole file-archive.

By a single click on one of the main genres the user gets additional information in the upper right window on the one hand and of course a file-listing in the lower left window on the other hand. Double clicking a genre in the main window brings the user straight to a sub-level containing the corresponding sub-genres again shown in the main window. Through the path shown on top of the main window the system allows to easily jump back to wherever necessary out of every level.

Further on the user is able to read out file-specific information like Title, Interpret,
Album, Year, Duration & Bit-Rate through a click on the particular file listed below, but is also allowed to save a play-list from the files selected via the file-explorer.

Following feature turns the visualization kit into a rather complex and outstanding tool. By default the tool sorts the input data through a given hierarchy according to the main genres and alternatively to the sub-genres following the ID3-Tags. Additionally the User has the ability to change this system and to create his own view according to his point of interest. That means that he is able to change the main genres and the sub-genres as well. As there are always different views especially on the terms of sub-genres the user is even allowed to change the structure of the given categories. That means that one is permitted to extract an ambiguous sub-genre to directly attach it to another genre. This will be able via the right middle window showing the sub-genres and their actual belongings as well. Probably the system will give the possibility to cancel unmeant genres at all.

=== Visual Mapping ===

*Due to the enormous number of music-genres ID3-Tags provide we will probably choose a tree structure in addition to the text-based genre-navigator on the right in lower levels. This structure should be similar to a hyperbolic tree.

Dimension "Genre-Multitude" --> visual attribute "Tree Branches"

*To afford best visualization we are right now not sure whether we should display the percentage-rate in bars as shown in the fake-screenshot below, or should rather use a Bubble-Chart, where the size of the bubbles is determined by the values of occurrence per genre.

Dimension "File-Occurrence" --> visual attribute "Area"

=== Specification of used Techniques / applied Principles ===

*Hyperbolic Trees:
(s. slide 18, 19, 20 of Info_Vis4.pdf handed out in the course “188.305 VO Informationsvisualisierung”)

Due to the enormous number of music-genres ID-3 Tags provide we probably choose a tree structure
similar to a hyperbolic tree to visualize the raw data extracted from the corresponding mp3-archiv.

*Bubble Chart:
(s. similar to bubble-maps, slide 52 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung, http://peltiertech.com/Excel/ChartsHowTo/HowToBubble.html)

Bubble chart, where the size of the bubbles is determined by the values of occurrence.

*Details on demand:
(s. slide 55 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Used several times. (File-Explorer, Genre-Information, ...)

*Linking & Brushing:
(s. slide 100 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Detail Windows containing percentage-distribution according to the specific genre

*Focus & Context: Tiled Multi-Level Browser
(slide 69 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Overview Window, Details on Demand Window

=== Possibilities of User-Interaction ===

* Assemble information about occurence-percentage per genre
* Assemble information according to ID3-Tags
* Group genres
* Build paths
* Build new tree
* Modify leaves
* Save play-list

=== Mockup / Fake Screenshot ===

[[Image:MP3_Viewer.gif]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 3 - Design

2005-11-22T17:29:52Z

UE-InfoVis0506 0026030: /* Intended Purpose of our Visualization */

== Specification of the Application Area and the given Dataset ==
== Analysis of the Target User Group ==

=== Who should use this kind of visualization technique? ===

This visualization technique is mainly meant for the 'end-users', that is someone who collects lots of mp3s. Our visualization should help the user to get an overview of his collection and discover coherences between . The user should also get some more information about his collection, that he did not know before or that he could not find in any usual representation.

=== What are the characteristics of the target group? ===

People of this group are musicenthusiasts. They have more than thousands of mp3s on their harddisk, and love it to collect mp3s. This people mainly receive their mp3s from the internet instead of buying CDs, because they like to see their whole collection at a glance and to browse through it in many different ways.

=== Are there any known / often used Methods / Visualisation Techniques? ===

No, until know only the textbased listing-technique is used.

== Intended Purpose of our Visualization ==

=== What should be achieved with this visualization? ===
It should be achieved a better information representation of the mp3s by combining several ID3-tags. The representation of the data and the contained information should be effective, precise and self-explanatory, so that the user can get a good and expressive overview of his collection and the contained information.

== Proposal of Design ==

=== Types of Visualization applied ===

As there are no comparable features implemented in existing mp3 archival storage systems we decided to implement a visualization kit exploring the quantitative distribution of an mp3-archive according to the different genres. As a default the main window shows several genres the authors considered to be the right ones for music main-genres in the root directory including their percentage-rate corresponding to the whole file-archive.

By a single click on one of the main genres the user gets additional information in the upper right window on the one hand and of course a file-listing in the lower left window on the other hand. Double clicking a genre in the main window brings the user straight to a sub-level containing the corresponding sub-genres again shown in the main window. Through the path shown on top of the main window the system allows to easily jump back to wherever necessary out of every level.

Further on the user is able to read out file-specific information like Title, Interpret,
Album, Year, Duration & Bit-Rate through a click on the particular file listed below, but is also allowed to save a play-list from the files selected via the file-explorer.

Following feature turns the visualization kit into a rather complex and outstanding tool. By default the tool sorts the input data through a given hierarchy according to the main genres and alternatively to the sub-genres following the ID3-Tags. Additionally the User has the ability to change this system and to create his own view according to his point of interest. That means that he is able to change the main genres and the sub-genres as well. As there are always different views especially on the terms of sub-genres the user is even allowed to change the structure of the given categories. That means that one is permitted to extract an ambiguous sub-genre to directly attach it to another genre. This will be able via the right middle window showing the sub-genres and their actual belongings as well. Probably the system will give the possibility to cancel unmeant genres at all.

=== Visual Mapping ===

*Due to the enormous number of music-genres ID3-Tags provide we will probably choose a tree structure in addition to the text-based genre-navigator on the right in lower levels. This structure should be similar to a hyperbolic tree.

Dimension "Genre-Multitude" --> visual attribute "Tree Branches"

*To afford best visualization we are right now not sure whether we should display the percentage-rate in bars as shown in the fake-screenshot below, or should rather use a Bubble-Chart, where the size of the bubbles is determined by the values of occurrence per genre.

Dimension "File-Occurrence" --> visual attribute "Area"

=== Specification of used Techniques / applied Principles ===

*Hyperbolic Trees:
(s. slide 18, 19, 20 of Info_Vis4.pdf handed out in the course “188.305 VO Informationsvisualisierung”)

Due to the enormous number of music-genres ID-3 Tags provide we probably choose a tree structure
similar to a hyperbolic tree to visualize the raw data extracted from the corresponding mp3-archiv.

*Bubble Chart:
(s. similar to bubble-maps, slide 52 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung, http://peltiertech.com/Excel/ChartsHowTo/HowToBubble.html)

Bubble chart, where the size of the bubbles is determined by the values of occurrence.

*Details on demand:
(s. slide 55 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Used several times. (File-Explorer, Genre-Information, ...)

*Linking & Brushing:
(s. slide 100 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Detail Windows containing percentage-distribution according to the specific genre

*Focus & Context: Tiled Multi-Level Browser
(slide 69 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Overview Window, Details on Demand Window

=== Possibilities of User-Interaction ===

* Assemble information about occurence-percentage per genre
* Assemble information according to ID3-Tags
* Group genres
* Build paths
* Build new tree
* Modify leaves
* Save play-list

=== Mockup / Fake Screenshot ===

[[Image:MP3_Viewer.gif]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 3 - Design

2005-11-22T17:04:27Z

UE-InfoVis0506 0026030: /* What are the characteristics of this group? */

== Specification of the Application Area and the given Dataset ==
== Analysis of the Target User Group ==

=== Who should use this kind of visualization technique? ===

This visualization technique is mainly meant for the 'end-users', that is someone who collects lots of mp3s. Our visualization should help the user to get an overview of his collection and discover coherences between . The user should also get some more information about his collection, that he did not know before or that he could not find in any usual representation.

=== What are the characteristics of the target group? ===

People of this group are musicenthusiasts. They have more than thousands of mp3s on their harddisk, and love it to collect mp3s. This people mainly receive their mp3s from the internet instead of buying CDs, because they like to see their whole collection at a glance and to browse through it in many different ways.

=== Are there any known / often used Methods / Visualisation Techniques? ===

No, until know only the textbased listing-technique is used.

== Intended Purpose of our Visualization ==
== Proposal of Design ==

=== Types of Visualization applied ===

As there are no comparable features implemented in existing mp3 archival storage systems we decided to implement a visualization kit exploring the quantitative distribution of an mp3-archive according to the different genres. As a default the main window shows several genres the authors considered to be the right ones for music main-genres in the root directory including their percentage-rate corresponding to the whole file-archive.

By a single click on one of the main genres the user gets additional information in the upper right window on the one hand and of course a file-listing in the lower left window on the other hand. Double clicking a genre in the main window brings the user straight to a sub-level containing the corresponding sub-genres again shown in the main window. Through the path shown on top of the main window the system allows to easily jump back to wherever necessary out of every level.

Further on the user is able to read out file-specific information like Title, Interpret,
Album, Year, Duration & Bit-Rate through a click on the particular file listed below, but is also allowed to save a play-list from the files selected via the file-explorer.

Following feature turns the visualization kit into a rather complex and outstanding tool. By default the tool sorts the input data through a given hierarchy according to the main genres and alternatively to the sub-genres following the ID3-Tags. Additionally the User has the ability to change this system and to create his own view according to his point of interest. That means that he is able to change the main genres and the sub-genres as well. As there are always different views especially on the terms of sub-genres the user is even allowed to change the structure of the given categories. That means that one is permitted to extract an ambiguous sub-genre to directly attach it to another genre. This will be able via the right middle window showing the sub-genres and their actual belongings as well. Probably the system will give the possibility to cancel unmeant genres at all.

=== Visual Mapping ===

*Due to the enormous number of music-genres ID3-Tags provide we will probably choose a tree structure in addition to the text-based genre-navigator on the right in lower levels. This structure should be similar to a hyperbolic tree.

Dimension "Genre-Multitude" --> visual attribute "Tree Branches"

*To afford best visualization we are right now not sure whether we should display the percentage-rate in bars as shown in the fake-screenshot below, or should rather use a Bubble-Chart, where the size of the bubbles is determined by the values of occurrence per genre.

Dimension "File-Occurrence" --> visual attribute "Area"

=== Specification of used Techniques / applied Principles ===

*Hyperbolic Trees:
(s. slide 18, 19, 20 of Info_Vis4.pdf handed out in the course “188.305 VO Informationsvisualisierung”)

Due to the enormous number of music-genres ID-3 Tags provide we probably choose a tree structure
similar to a hyperbolic tree to visualize the raw data extracted from the corresponding mp3-archiv.

*Bubble Chart:
(s. similar to bubble-maps, slide 52 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung, http://peltiertech.com/Excel/ChartsHowTo/HowToBubble.html)

Bubble chart, where the size of the bubbles is determined by the values of occurrence.

*Details on demand:
(s. slide 55 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Used several times. (File-Explorer, Genre-Information, ...)

*Linking & Brushing:
(s. slide 100 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Detail Windows containing percentage-distribution according to the specific genre

*Focus & Context: Tiled Multi-Level Browser
(slide 69 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Overview Window, Details on Demand Window

=== Possibilities of User-Interaction ===

* Assemble information about occurence-percentage per genre
* Assemble information according to ID3-Tags
* Group genres
* Build paths
* Build new tree
* Modify leaves
* Save play-list

=== Mockup / Fake Screenshot ===

[[Image:MP3_Viewer.gif]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 3 - Design

2005-11-22T16:56:41Z

UE-InfoVis0506 0026030: /* Analysis of the Target User Group */

== Specification of the Application Area and the given Dataset ==
== Analysis of the Target User Group ==

=== Who should use this kind of visualization technique? ===

This visualization technique is mainly meant for the 'end-users', that is someone who collects lots of mp3s. Our visualization should help the user to get an overview of his collection and discover coherences between . The user should also get some more information about his collection, that he did not know before or that he could not find in any usual representation.

=== What are the characteristics of this group? ===

== Intended Purpose of our Visualization ==
== Proposal of Design ==

=== Types of Visualization applied ===

As there are no comparable features implemented in existing mp3 archival storage systems we decided to implement a visualization kit exploring the quantitative distribution of an mp3-archive according to the different genres. As a default the main window shows several genres the authors considered to be the right ones for music main-genres in the root directory including their percentage-rate corresponding to the whole file-archive.

By a single click on one of the main genres the user gets additional information in the upper right window on the one hand and of course a file-listing in the lower left window on the other hand. Double clicking a genre in the main window brings the user straight to a sub-level containing the corresponding sub-genres again shown in the main window. Through the path shown on top of the main window the system allows to easily jump back to wherever necessary out of every level.

Further on the user is able to read out file-specific information like Title, Interpret,
Album, Year, Duration & Bit-Rate through a click on the particular file listed below, but is also allowed to save a play-list from the files selected via the file-explorer.

Following feature turns the visualization kit into a rather complex and outstanding tool. By default the tool sorts the input data through a given hierarchy according to the main genres and alternatively to the sub-genres following the ID3-Tags. Additionally the User has the ability to change this system and to create his own view according to his point of interest. That means that he is able to change the main genres and the sub-genres as well. As there are always different views especially on the terms of sub-genres the user is even allowed to change the structure of the given categories. That means that one is permitted to extract an ambiguous sub-genre to directly attach it to another genre. This will be able via the right middle window showing the sub-genres and their actual belongings as well. Probably the system will give the possibility to cancel unmeant genres at all.

=== Visual Mapping ===

*Due to the enormous number of music-genres ID3-Tags provide we will probably choose a tree structure in addition to the text-based genre-navigator on the right in lower levels. This structure should be similar to a hyperbolic tree.

Dimension "Genre-Multitude" --> visual attribute "Tree Branches"

*To afford best visualization we are right now not sure whether we should display the percentage-rate in bars as shown in the fake-screenshot below, or should rather use a Bubble-Chart, where the size of the bubbles is determined by the values of occurrence per genre.

Dimension "File-Occurrence" --> visual attribute "Area"

=== Specification of used Techniques / applied Principles ===

*Hyperbolic Trees:
(s. slide 18, 19, 20 of Info_Vis4.pdf handed out in the course “188.305 VO Informationsvisualisierung”)

Due to the enormous number of music-genres ID-3 Tags provide we probably choose a tree structure
similar to a hyperbolic tree to visualize the raw data extracted from the corresponding mp3-archiv.

*Bubble Chart:
(s. similar to bubble-maps, slide 52 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung, http://peltiertech.com/Excel/ChartsHowTo/HowToBubble.html)

Bubble chart, where the size of the bubbles is determined by the values of occurrence.

*Details on demand:
(s. slide 55 of Info_Vis4.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Used several times. (File-Explorer, Genre-Information, ...)

*Linking & Brushing:
(s. slide 100 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Detail Windows containing percentage-distribution according to the specific genre

*Focus & Context: Tiled Multi-Level Browser
(slide 69 of Info_Vis0.pdf handed out in the course 188.305 VO Informationsvisualisierung)

Overview Window, Details on Demand Window

=== Possibilities of User-Interaction ===

* Assemble information about occurence-percentage per genre
* Assemble information according to ID3-Tags
* Group genres
* Build paths
* Build new tree
* Modify leaves
* Save play-list

=== Mockup / Fake Screenshot ===

[[Image:MP3_Viewer.gif]]

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 2

2005-11-01T11:33:15Z

UE-InfoVis0506 0026030: /* Poor Graphic */

== Poor Graphic ==
[[Image:Perceptualedge03slicerdicer.gif|none|thumb|600px|SlicerDicer's Sales Compared to Other Products (click on image for larger version)]]

Here we have got an example for a low [http://www.infovis-wiki.net/index.php/Teaching:TUW_-_UE_InfoVis_WS_2005/06_-_Gruppe_01_-_Aufgabe_1_-_Data-Ink_Ratio data-ink ratio]. The data-ink ratio is the proportion of the ink that is used to present actual data, without redundancy, compared to the total amount of int used in the entire display. The goal is to receive the highest possible data-ink ration (a value close to 1.0 or 100%).
 
The data-ink ratio is defined as the percentage: (100*data-ink)/(total ink used on the graph) [Tufte, 1983]
 
First off all we have to distinguish between data-ink and non-data-ink:
* data-ink is the essential non-erasable ink used to present the data
* non-data-ink is the redundant ink used to ellaborate or decorate the graph [Hunt, 1997]
 
To enhance the data-ink ratio we have to
* remove unnecessary non-data-ink
* regularize the remaining non-data-ink and
* emphasize the most important data-ink. [Few, 2004]
 
How much of the in in our example is completely unnecessary and could be removed?
* the border around the legend
* the border around the plot area
* the grid
* the black borders around the bars, because they have no value
* the repitition of the dollar-sign for each revenue value is redundant. It would be better to replace them by simply adding the label "U.S. $" to the vertical axis.

==References:==

[Few, 2004] Stephen Few, Elegance through simplicity, Faculty of Information and Media Studies at the University of Western Ontario, Last updated: October 16, 2004, Retrieved at: October 30, 2005, http://www.intelligententerprise.com/showArticle.jhtml;jsessionid=N2ATDQWY5VYKSQSNDBGCKHSCJUMEKJVN?articleID=49400920
 
[Tufte, 1983] Tufte, Edward. ''The Visual Display of Quantitative Information''. Graphics Press, Cheshire, 1983.
 
[Hunt, 1997] Neville Hunt, Data Ink. Created at: 1997. Retrieved at: October 30, 2005. http://home.ched.coventry.ac.uk/Volume/contents.htm.

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 2

2005-11-01T11:33:00Z

UE-InfoVis0506 0026030: /* References: */

== Poor Graphic ==
[[Image:Perceptualedge03slicerdicer.gif|none|thumb|600px|SlicerDicer's Sales Compared to Other Products (click on image for larger version)]]

Here we have got an example for a low [http://www.infovis-wiki.net/index.php/Teaching:TUW_-_UE_InfoVis_WS_2005/06_-_Gruppe_01_-_Aufgabe_1_-_Data-Ink_Ratio data-ink ratio]. The data-ink ratio is the proportion of the ink that is used to present actual data, without redundancy, compared to the total amount of int used in the entire display. The goal is to receive the highest possible data-ink ration (a value close to 1.0 or 100%).
 
The data-ink ratio is defined as the percentage: (100*data-ink)/(total ink used on the graph) [Tufte, 1983]
 
First off all we have to distinguish between data-ink and non-data-ink:
* data-ink is the essential non-erasable ink used to present the data
* non-data-ink is the redundant ink used to ellaborate or decorate the graph [2]
 
To enhance the data-ink ratio we have to
* remove unnecessary non-data-ink
* regularize the remaining non-data-ink and
* emphasize the most important data-ink. [Few, 2004]
 
How much of the in in our example is completely unnecessary and could be removed?
* the border around the legend
* the border around the plot area
* the grid
* the black borders around the bars, because they have no value
* the repitition of the dollar-sign for each revenue value is redundant. It would be better to replace them by simply adding the label "U.S. $" to the vertical axis.

==References:==

[Few, 2004] Stephen Few, Elegance through simplicity, Faculty of Information and Media Studies at the University of Western Ontario, Last updated: October 16, 2004, Retrieved at: October 30, 2005, http://www.intelligententerprise.com/showArticle.jhtml;jsessionid=N2ATDQWY5VYKSQSNDBGCKHSCJUMEKJVN?articleID=49400920
 
[Tufte, 1983] Tufte, Edward. ''The Visual Display of Quantitative Information''. Graphics Press, Cheshire, 1983.
 
[Hunt, 1997] Neville Hunt, Data Ink. Created at: 1997. Retrieved at: October 30, 2005. http://home.ched.coventry.ac.uk/Volume/contents.htm.

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 2

2005-11-01T11:32:12Z

UE-InfoVis0506 0026030: /* References: */

== Poor Graphic ==
[[Image:Perceptualedge03slicerdicer.gif|none|thumb|600px|SlicerDicer's Sales Compared to Other Products (click on image for larger version)]]

Here we have got an example for a low [http://www.infovis-wiki.net/index.php/Teaching:TUW_-_UE_InfoVis_WS_2005/06_-_Gruppe_01_-_Aufgabe_1_-_Data-Ink_Ratio data-ink ratio]. The data-ink ratio is the proportion of the ink that is used to present actual data, without redundancy, compared to the total amount of int used in the entire display. The goal is to receive the highest possible data-ink ration (a value close to 1.0 or 100%).
 
The data-ink ratio is defined as the percentage: (100*data-ink)/(total ink used on the graph) [Tufte, 1983]
 
First off all we have to distinguish between data-ink and non-data-ink:
* data-ink is the essential non-erasable ink used to present the data
* non-data-ink is the redundant ink used to ellaborate or decorate the graph [2]
 
To enhance the data-ink ratio we have to
* remove unnecessary non-data-ink
* regularize the remaining non-data-ink and
* emphasize the most important data-ink. [Few, 2004]
 
How much of the in in our example is completely unnecessary and could be removed?
* the border around the legend
* the border around the plot area
* the grid
* the black borders around the bars, because they have no value
* the repitition of the dollar-sign for each revenue value is redundant. It would be better to replace them by simply adding the label "U.S. $" to the vertical axis.

==References:==

[Few, 2004] Stephen Few, Elegance through simplicity, Faculty of Information and Media Studies at the University of Western Ontario, Last updated: October 16, 2004, Retrieved at: October 30, 2005, http://www.intelligententerprise.com/showArticle.jhtml;jsessionid=N2ATDQWY5VYKSQSNDBGCKHSCJUMEKJVN?articleID=49400920
 
[Tufte, 1983] Tufte, Edward. ''The Visual Display of Quantitative Information''. Graphics Press, Cheshire, 1983.

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 2

2005-11-01T11:31:14Z

UE-InfoVis0506 0026030: /* Poor Graphic */

== Poor Graphic ==
[[Image:Perceptualedge03slicerdicer.gif|none|thumb|600px|SlicerDicer's Sales Compared to Other Products (click on image for larger version)]]

Here we have got an example for a low [http://www.infovis-wiki.net/index.php/Teaching:TUW_-_UE_InfoVis_WS_2005/06_-_Gruppe_01_-_Aufgabe_1_-_Data-Ink_Ratio data-ink ratio]. The data-ink ratio is the proportion of the ink that is used to present actual data, without redundancy, compared to the total amount of int used in the entire display. The goal is to receive the highest possible data-ink ration (a value close to 1.0 or 100%).
 
The data-ink ratio is defined as the percentage: (100*data-ink)/(total ink used on the graph) [Tufte, 1983]
 
First off all we have to distinguish between data-ink and non-data-ink:
* data-ink is the essential non-erasable ink used to present the data
* non-data-ink is the redundant ink used to ellaborate or decorate the graph [2]
 
To enhance the data-ink ratio we have to
* remove unnecessary non-data-ink
* regularize the remaining non-data-ink and
* emphasize the most important data-ink. [Few, 2004]
 
How much of the in in our example is completely unnecessary and could be removed?
* the border around the legend
* the border around the plot area
* the grid
* the black borders around the bars, because they have no value
* the repitition of the dollar-sign for each revenue value is redundant. It would be better to replace them by simply adding the label "U.S. $" to the vertical axis.

==References:==

[Few, 2004] Stephen Few, Elegance through simplicity, Faculty of Information and Media Studies at the University of Western Ontario, Last updated: October 16, 2004, Retrieved at: October 30, 2005, http://www.intelligententerprise.com/showArticle.jhtml;jsessionid=N2ATDQWY5VYKSQSNDBGCKHSCJUMEKJVN?articleID=49400920

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 2

2005-11-01T11:30:14Z

UE-InfoVis0506 0026030: /* References: */

== Poor Graphic ==
[[Image:Perceptualedge03slicerdicer.gif|none|thumb|600px|SlicerDicer's Sales Compared to Other Products (click on image for larger version)]]

Here we have got an example for a low [http://www.infovis-wiki.net/index.php/Teaching:TUW_-_UE_InfoVis_WS_2005/06_-_Gruppe_01_-_Aufgabe_1_-_Data-Ink_Ratio data-ink ratio]. The data-ink ratio is the proportion of the ink that is used to present actual data, without redundancy, compared to the total amount of int used in the entire display. The goal is to receive the highest possible data-ink ration (a value close to 1.0 or 100%).
 
The data-ink ratio is defined as the percentage: (100*data-ink)/(total ink used on the graph) [Few, 2004]
 
First off all we have to distinguish between data-ink and non-data-ink:
* data-ink is the essential non-erasable ink used to present the data
* non-data-ink is the redundant ink used to ellaborate or decorate the graph [2]
 
To enhance the data-ink ratio we have to
* remove unnecessary non-data-ink
* regularize the remaining non-data-ink and
* emphasize the most important data-ink. [1]
 
How much of the in in our example is completely unnecessary and could be removed?
* the border around the legend
* the border around the plot area
* the grid
* the black borders around the bars, because they have no value
* the repitition of the dollar-sign for each revenue value is redundant. It would be better to replace them by simply adding the label "U.S. $" to the vertical axis.

==References:==

[Few, 2004] Stephen Few, Elegance through simplicity, Faculty of Information and Media Studies at the University of Western Ontario, Last updated: October 16, 2004, Retrieved at: October 30, 2005, http://www.intelligententerprise.com/showArticle.jhtml;jsessionid=N2ATDQWY5VYKSQSNDBGCKHSCJUMEKJVN?articleID=49400920

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 2

2005-11-01T11:29:01Z

UE-InfoVis0506 0026030: /* Poor Graphic */

== Poor Graphic ==
[[Image:Perceptualedge03slicerdicer.gif|none|thumb|600px|SlicerDicer's Sales Compared to Other Products (click on image for larger version)]]

Here we have got an example for a low [http://www.infovis-wiki.net/index.php/Teaching:TUW_-_UE_InfoVis_WS_2005/06_-_Gruppe_01_-_Aufgabe_1_-_Data-Ink_Ratio data-ink ratio]. The data-ink ratio is the proportion of the ink that is used to present actual data, without redundancy, compared to the total amount of int used in the entire display. The goal is to receive the highest possible data-ink ration (a value close to 1.0 or 100%).
 
The data-ink ratio is defined as the percentage: (100*data-ink)/(total ink used on the graph) [Few, 2004]
 
First off all we have to distinguish between data-ink and non-data-ink:
* data-ink is the essential non-erasable ink used to present the data
* non-data-ink is the redundant ink used to ellaborate or decorate the graph [2]
 
To enhance the data-ink ratio we have to
* remove unnecessary non-data-ink
* regularize the remaining non-data-ink and
* emphasize the most important data-ink. [1]
 
How much of the in in our example is completely unnecessary and could be removed?
* the border around the legend
* the border around the plot area
* the grid
* the black borders around the bars, because they have no value
* the repitition of the dollar-sign for each revenue value is redundant. It would be better to replace them by simply adding the label "U.S. $" to the vertical axis.

==References:==

[Craven, 2001a] Tim Craven, LIS 504 - Terms with definitions, Faculty of Information and Media Studies at the University of Western Ontario, Last updated: October 1, 2001, Retrieved at: October 24, 2005, http://instruct.uwo.ca/fim-lis/504/504ter.htm#l

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 2

2005-11-01T11:27:02Z

UE-InfoVis0506 0026030: /* Poor Graphic */

== Poor Graphic ==
[[Image:Perceptualedge03slicerdicer.gif|none|thumb|600px|SlicerDicer's Sales Compared to Other Products (click on image for larger version)]]

Here we have got an example for a low [http://www.infovis-wiki.net/index.php/Teaching:TUW_-_UE_InfoVis_WS_2005/06_-_Gruppe_01_-_Aufgabe_1_-_Data-Ink_Ratio data-ink ratio]. The data-ink ratio is the proportion of the ink that is used to present actual data, without redundancy, compared to the total amount of int used in the entire display. The goal is to receive the highest possible data-ink ration (a value close to 1.0 or 100%).
 
The data-ink ratio is defined as the percentage: (100*data-ink)/(total ink used on the graph) [1]
 
First off all we have to distinguish between data-ink and non-data-ink:
* data-ink is the essential non-erasable ink used to present the data
* non-data-ink is the redundant ink used to ellaborate or decorate the graph [2]
 
To enhance the data-ink ratio we have to
* remove unnecessary non-data-ink
* regularize the remaining non-data-ink and
* emphasize the most important data-ink. [1]
 
How much of the in in our example is completely unnecessary and could be removed?
* the border around the legend
* the border around the plot area
* the grid
* the black borders around the bars, because they have no value
* the repitition of the dollar-sign for each revenue value is redundant. It would be better to replace them by simply adding the label "U.S. $" to the vertical axis.

==References:==

[Craven, 2001a] Tim Craven, LIS 504 - Terms with definitions, Faculty of Information and Media Studies at the University of Western Ontario, Last updated: October 1, 2001, Retrieved at: October 24, 2005, http://instruct.uwo.ca/fim-lis/504/504ter.htm#l

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 2

2005-10-31T09:36:06Z

UE-InfoVis0506 0026030: /* Poor Graphic */

== Poor Graphic ==
[[Image:Perceptualedge03slicerdicer.gif|none|thumb|600px|SlicerDicer's Sales Compared to Other Products (click on image for larger version)]]

Here we have got an example for a low [http://www.infovis-wiki.net/index.php/Teaching:TUW_-_UE_InfoVis_WS_2005/06_-_Gruppe_01_-_Aufgabe_1_-_Data-Ink_Ratio data-ink ratio]. The data-ink ratio is the proportion of the ink that is used to present actual data, without redundancy, compared to the total amount of int used in the entire display. The goal is to receive the highest possible data-ink ration (a value close to 1.0 or 100%).
 
The data-ink ratio is defined as the percentage: (100*data-ink)/(total ink used on the graph) [1]
 
First off all we have to distinguish between data-ink and non-data-ink:
* data-ink is the essential non-erasable ink used to present the data
* non-data-ink is the redundant ink used to ellaborate or decorate the graph [2]
 
To enhance the data-ink ratio we have to
* remove unnecessary non-data-ink
* regularize the remaining non-data-ink and
* emphasize the most important data-ink. [1]
 
How much of the in in our example is completely unnecessary and could be removed?
* the border around the legend
* the border around the plot area
* the grid
* the black borders around the bars, because they have no value
* the repitition of the dollar-sign for each revenue value is redundant. It would be better to replace them by simply adding the label "U.S. $" to the vertical axis.

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 2

2005-10-31T09:35:43Z

UE-InfoVis0506 0026030: /* Poor Graphic */

== Poor Graphic ==
[[Image:Perceptualedge03slicerdicer.gif|none|thumb|600px|SlicerDicer's Sales Compared to Other Products (click on image for larger version)]]

Here we have got an example for a low [http://www.infovis-wiki.net/index.php/Teaching:TUW_-_UE_InfoVis_WS_2005/06_-_Gruppe_01_-_Aufgabe_1_-_Data-Ink_Ratio data-ink ratio]. The data-ink ratio is the proportion of the ink that is used to present actual data, without redundancy, compared to the total amount of int used in the entire display. The goal is to receive the highest possible data-ink ration (a value close to 1.0 or 100%).
 
The data-ink ratio is defined as the percentage: (100*data-ink)/(total ink used on the graph) [1]
 
 
First off all we have to distinguish between data-ink and non-data-ink:
* data-ink is the essential non-erasable ink used to present the data
* non-data-ink is the redundant ink used to ellaborate or decorate the graph [2]
 
 
To enhance the data-ink ratio we have to
* remove unnecessary non-data-ink
* regularize the remaining non-data-ink and
* emphasize the most important data-ink. [1]
 
 
How much of the in in our example is completely unnecessary and could be removed?
* the border around the legend
* the border around the plot area
* the grid
* the black borders around the bars, because they have no value
* the repitition of the dollar-sign for each revenue value is redundant. It would be better to replace them by simply adding the label "U.S. $" to the vertical axis.

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 2

2005-10-31T09:32:30Z

UE-InfoVis0506 0026030: /* Poor Graphic */

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 2

2005-10-31T09:30:23Z

UE-InfoVis0506 0026030: /* Poor Graphic */

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 2

2005-10-31T09:29:12Z

UE-InfoVis0506 0026030: /* Poor Graphic */

== Poor Graphic ==
[[Image:Perceptualedge03slicerdicer.gif|none|thumb|600px|SlicerDicer's Sales Compared to Other Products (click on image for larger version)]]

Here we have got an example for a low [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G1 - Aufgabe 1 - data-ink ratio|data-ink ratio]]. The data-ink ratio is the proportion of the ink that is used to present actual data, without redundancy, compared to the total amount of int used in the entire display. The goal is to receive the highest possible data-ink ration (a value close to 1.0 or 100%).
 
The data-ink ratio is defined as the percentage: (100*data-ink)/(total ink used on the graph)

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 2

2005-10-31T09:28:55Z

UE-InfoVis0506 0026030: /* Poor Graphic */

== Poor Graphic ==
[[Image:Perceptualedge03slicerdicer.gif|none|thumb|600px|SlicerDicer's Sales Compared to Other Products (click on image for larger version)]]

Here we have got an example for a low [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G1 - Aufgabe 1|data-ink ratio]]. The data-ink ratio is the proportion of the ink that is used to present actual data, without redundancy, compared to the total amount of int used in the entire display. The goal is to receive the highest possible data-ink ration (a value close to 1.0 or 100%).
 
The data-ink ratio is defined as the percentage: (100*data-ink)/(total ink used on the graph)

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 2

2005-10-31T09:28:07Z

UE-InfoVis0506 0026030: /* Poor Graphic */

== Poor Graphic ==
[[Image:Perceptualedge03slicerdicer.gif|none|thumb|600px|SlicerDicer's Sales Compared to Other Products (click on image for larger version)]]

Here we have got an example for a low [[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G1 - Aufgabe 2|data-ink ratio]]. The data-ink ratio is the proportion of the ink that is used to present actual data, without redundancy, compared to the total amount of int used in the entire display. The goal is to receive the highest possible data-ink ration (a value close to 1.0 or 100%).
 
The data-ink ratio is defined as the percentage: (100*data-ink)/(total ink used on the graph)

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 2

2005-10-31T09:26:07Z

UE-InfoVis0506 0026030: /* Poor Graphic */

== Poor Graphic ==
[[Image:Perceptualedge03slicerdicer.gif|none|thumb|600px|SlicerDicer's Sales Compared to Other Products (click on image for larger version)]]

Here we have got an example for a low [[data-ink ratio]][[Link title]]. The data-ink ratio is the proportion of the ink that is used to present actual data, without redundancy, compared to the total amount of int used in the entire display. The goal is to receive the highest possible data-ink ration (a value close to 1.0 or 100%).
 
The data-ink ratio is defined as the percentage: (100*data-ink)/(total ink used on the graph)

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 2

2005-10-31T09:04:52Z

UE-InfoVis0506 0026030: /* Poor Graphic */

== Poor Graphic ==
[[Image:Perceptualedge03slicerdicer.gif|none|thumb|600px|SlicerDicer's Sales Compared to Other Products (click on image for larger version)]]

Here we have got an example for a low data-ink ratio. The data-ink ratio is the proportion of the ink that is used to present actual data, without redundancy, compared to the total amount of int used in the entire display. The goal is to receive the highest possible data-ink ration (a value close to 1.0 or 100%).
 
The data-ink ratio is defined as the percentage: (100*data-ink)/(total ink used on the graph)

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 2

2005-10-31T09:04:25Z

UE-InfoVis0506 0026030: /* Poor Graphic */

== Poor Graphic ==
[[Image:Perceptualedge03slicerdicer.gif|none|thumb|600px|SlicerDicer's Sales Compared to Other Products (click on image for larger version)]]

Here we have got an example for a low data-ink ratio. The data-ink ratio is the proportion of the ink that is used to present actual data, without redundancy, compared to the total amount of int used in the entire display. The goal is to receive the highest possible data-ink ration (a value close to 1.0 or 100%).
The data-ink ratio is defined as the percentage: (100*data-ink)/(total ink used on the graph)

File:Perceptualedge03slicerdicer.gif

2005-10-31T09:03:57Z

UE-InfoVis0506 0026030: /* Summary */

== Summary ==

== Copyright status ==

== Source ==
[Perceptual Edge, 2003] Perceptual Edge, SlicerDicer's Sales Compared to Other Products, Design Example #6. Created at: 2003. Retrieved at: Oct. 10, 2005

File:Perceptualedge03slicerdicer.gif

2005-10-31T09:00:57Z

UE-InfoVis0506 0026030: /* Summary */

== Summary ==
Here we have got an example for a low data-ink ratio. The data-ink ratio is the proportion of the ink that is used to present actual data, without redundancy, compared to the total amount of int used in the entire display. The goal is to receive the highest possible data-ink ration (a value close to 1.0 or 100%).
The data-ink ratio is defined as the percentage: (100*data-ink)/(total ink used on the graph)

== Copyright status ==

== Source ==
[Perceptual Edge, 2003] Perceptual Edge, SlicerDicer's Sales Compared to Other Products, Design Example #6. Created at: 2003. Retrieved at: Oct. 10, 2005

File:Perceptualedge03slicerdicer.gif

2005-10-31T08:59:46Z

UE-InfoVis0506 0026030: /* Summary */

== Summary ==
Here we have got an example for a low data-ink ratio. The data-ink ratio is the proportion of the ink that is used to present actual data, without redundancy, compared to the total amount of int used in the entire display. The goal is to receive the highest possible data-ink ration (a value close to 1.0 or 100%). The data-ink ratio is defined as the percentage: (100*data-ink)/total ink used on the graph

== Copyright status ==

== Source ==
[Perceptual Edge, 2003] Perceptual Edge, SlicerDicer's Sales Compared to Other Products, Design Example #6. Created at: 2003. Retrieved at: Oct. 10, 2005

Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4

2005-10-16T16:01:18Z

UE-InfoVis0506 0026030: /* '''Gruppe G4''' */

== '''Gruppe G4''' ==

Die Gruppe G4 besteht bis jetzt aus den folgenden Mitgliedern: 
The following people are part of the G4 group by now:

* [[User:UE-InfoVis0506_0026030|Muster, Anna]]
* [[User:UE-InfoVis0506_9335259|Puchta, Jürgen]]
* [[User:UE-InfoVis0506_0026754|Rainer, Christian]]
* [[User:UE-InfoVis0506_0026629|Sölder, Christoph]]

---- 
[[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 0|Aufgabe 0]] 
[[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 1|Aufgabe 1]] 
[[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 2|Aufgabe 2]] 
[[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 3|Aufgabe 3]]

User:UE-InfoVis0506 0026030

2005-10-16T15:59:08Z

UE-InfoVis0506 0026030:

== Userpage e0026030 - 188.308 Informationsvisualisierung - WS 2005/06 ==
----
 
 
'''Personendaten'''
 
 
* Nachname, Vorname: Muster, Anna
* Matrikelnummer: e0026030
* Studienrichtung: Magisterstudium Medieninformatik
* Studienkennzahl: E 066 935
* Uebungsgruppe: Gruppe G4
* E-mail Adresse: anna_muster_at_gmx.at