InfoVis:Wiki - User contributions [en]

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

2005-12-20T23:00:38Z

UE-InfoVis0506 0026629:

== PROJECT: Interpret - Analyzer ==

==Authors==

Anna Muster 0026030 935

Jürgen Puchta 9335259 532

Christian Rainer 0026754 532

Christoph Sölder 0026629 532

== Task description ==

The task was to design a visualization technique based on a known applicationarea. For a better demonstration we had to implement a prototype. But before we could start, we had to analyze the target group, think about interaction-possibilities and the datatypes as well as the datastructure.
Since we have chosen the MP3 Archive Visualization, our job was the creation of a clearly arranged visualization for a big music archive consisting of several thousand files. We wanted to achieve a better information representation of the MP3s. Our visualization should help the user to get an overview of his collection and his listening-habits. The user will get information about the tracks, in reference to a special artist, which he often listens to and to those which he has never heard before.
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 music-style 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.
Our target was to design a representation of the data that is expressive, precise and self-explanatory.

== Implementation description ==

When the user opens the "Interpret-Analyser" he will be prompted via a text-message in the main-window to click on an artist/band in the right upper window. The artists/bands are sorted alphabetically and the subject of interest can be found by scrolling the window vertically. If the user chose an artist/band in the upper right window, the main window will visualize him following details on the demanded item:

*The x-axis shows the songs listed vertically by-publication-year generated out of the ID3-Data
*The y-axis shows the number of songs published per year according to the specific number in the users' iTunes-library

The respective maximum on the y-axis will give a first overview on how many songs the specific library contains per artist/band per year. For each song (= one data point) one horizontal bar is drawn along the y-axis. That means for example if the library contains 34 songs by the band "Queen" with publication-year "1985", 34 bars are drawn at the x-axis value "1985" along the vertical y-axis.
In addition to that each bar drawn vertically has a specific colour, representing the date when he was last played. As it is shown in our Mock-Up below the range goes from blue (representing songs that have not been played for a long time) to red (representing songs that have been played recently).
Further on the user can interact and influence the characteristic of the visualization by using a slider positioned in the lower right in the graphic below. Via the slider a more objective image can be drawn according to the actual point of interest. This slider with a value-range from "0" to "10" represents the counts how often a song was played. It allows setting a threshold. The default value is "3" and means that songs that were played less than 3 times do not appear coloured, but as grey bars vertically above the coloured ones along the y-axis.

== Screenshots ==

[[Image:G4_Interpret-Analyser.jpg]]

When the user opens the "Interpret-Analyser" he will be prompted via a text-message in the main-window to click on an artist/band in the right upper window. The artists/bands are sorted alphabetically and the subject of interest can be found by scrolling the window vertically. If the user chose an artist/band in the upper right window, the main window will visualize him following details on the demanded item:

*The x-axis shows the songs listed vertically by-publication-year generated out of the ID3-Data
*The y-axis shows the number of songs published per year according to the specific number in the users' iTunes-library

The respective maximum on the y-axis will give a first overview on how many songs the specific library contains per artist/band per year. For each song (= one data point) one horizontal bar is drawn along the y-axis. That means for example if the library contains 34 songs by the band "Queen" with publication-year "1985", 34 bars are drawn at the x-axis value "1985" along the vertical y-axis.
In addition to that each bar drawn vertically has a specific colour, representing the date when he was last played. As it is shown in our Mock-Up below the range goes from blue (representing songs that have not been played for a long time) to red (representing songs that have been played recently).
Further on the user can interact and influence the characteristic of the visualization by using a slider positioned in the lower right in the graphic below. Via the slider a more objective image can be drawn according to the actual point of interest. This slider with a value-range from "0" to "10" represents the counts how often a song was played. It allows setting a threshold. The default value is "3" and means that songs that were played less than 3 times do not appear coloured, but as grey bars vertically above the coloured ones along the y-axis.

== Toolkits and/or Libraries ==

we did not use any libraries and toolkits

== Links ==

[http://www.infovis-wiki.net/index.php/Teaching:TUW_-_UE_InfoVis_WS_2005/06_-_Gruppe_G4 Infoviswiki: Project: Group G4]

[http://www.infovis-wiki.net/index.php?title=Teaching:TUW_-_UE_InfoVis_WS_2005/06_-_Gruppe_G4_-_Aufgabe_3_-_Prototype Infovis: Our Presentation]

File:G4 Interpret-Analyser.jpg

2005-12-20T22:55:39Z

UE-InfoVis0506 0026629: Screenshot of the Prototype

== Beschreibung ==
Screenshot of the Prototype
== Copyright-Status ==

== Quelle ==
Christoph Sölder

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

2005-12-02T21:32:10Z

UE-InfoVis0506 0026629: /* Dataset Analysis */

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

Since we have chosen the MP3 Archive Visualization, our job will be the creation of a clearly arranged visualization for a big music archive consisting of several thousand files.

This can be achieved by using the already existing values of the container format ID3, additional attributes of the iTunes library, attributes of the music files themselves, as well as some system values.

Due the fact that these given sources already provide loads of different Information, we will try to create and present additional Information by combining some prior chosen values in a reasonable way.

Thus we have to keep in mind that ID3 for example theoretically indeed supports a huge amount of input values but in most cases only the most common values like Album, Interpret or Year are specified correctly.

Therefore we will only use some of these entries in our prototype.

=== Dataset Analysis ===

The values we will use in our project consist of nominal, discrete and ordinal data types and are for themselves all one-dimensional.

The table below shows a complete listing:

[[Image:tabelle_datentyp_g4.png]]

The complete data set is multi-dimensional and consists of all attributes listed above.

== 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

File:Tabelle datentyp g4.png

2005-12-02T21:29:21Z

UE-InfoVis0506 0026629: Table of data types used in the prototype

== Beschreibung ==
Table of data types used in the prototype
== Copyright-Status ==
free
== Quelle ==
Sölder Christoph

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

2005-12-02T19:51:21Z

UE-InfoVis0506 0026629: /* Dataset Analysis */

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

Since we have chosen the MP3 Archive Visualization, our job will be the creation of a clearly arranged visualization for a big music archive consisting of several thousand files.

This can be achieved by using the already existing values of the container format ID3, additional attributes of the iTunes library, attributes of the music files themselves, as well as some system values.

Due the fact that these given sources already provide loads of different Information, we will try to create and present additional Information by combining some prior chosen values in a reasonable way.

Thus we have to keep in mind that ID3 for example theoretically indeed supports a huge amount of input values but in most cases only the most common values like Album, Interpret or Year are specified correctly.

Therefore we will only use some of these entries in our prototype.

=== Dataset Analysis ===

The values we will use in our project consist of nominal, discrete and ordinal data types and are for themselves all one-dimensional.

The table below shows a complete listing:

The complete data set is multi-dimensional and consists of all attributes listed above.

== 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

2005-12-02T19:44:02Z

UE-InfoVis0506 0026629: /* Dataset Analysis */

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

Since we have chosen the MP3 Archive Visualization, our job will be the creation of a clearly arranged visualization for a big music archive consisting of several thousand files.

This can be achieved by using the already existing values of the container format ID3, additional attributes of the iTunes library, attributes of the music files themselves, as well as some system values.

Due the fact that these given sources already provide loads of different Information, we will try to create and present additional Information by combining some prior chosen values in a reasonable way.

Thus we have to keep in mind that ID3 for example theoretically indeed supports a huge amount of input values but in most cases only the most common values like Album, Interpret or Year are specified correctly.

Therefore we will only use some of these entries in our prototype.

=== Dataset Analysis ===

The values we will use in our project consist of nominal, discrete and ordinal data types. The values for themselves are all one-dimensional.

The table below shows a complete listing:

The complete data set is multi-dimensional and consists of all attributes listed above.

== 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

2005-12-02T19:43:15Z

UE-InfoVis0506 0026629: /* Application area Analysis */

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

Since we have chosen the MP3 Archive Visualization, our job will be the creation of a clearly arranged visualization for a big music archive consisting of several thousand files.

This can be achieved by using the already existing values of the container format ID3, additional attributes of the iTunes library, attributes of the music files themselves, as well as some system values.

Due the fact that these given sources already provide loads of different Information, we will try to create and present additional Information by combining some prior chosen values in a reasonable way.

Thus we have to keep in mind that ID3 for example theoretically indeed supports a huge amount of input values but in most cases only the most common values like Album, Interpret or Year are specified correctly.

Therefore we will only use some of these entries in our prototype.

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

2005-11-26T13:52:48Z

UE-InfoVis0506 0026629: /* Kopierverdacht + Korrektur */

'''Kommentar zum Konzept:'''

*Die Datentypen für die Attribute sind nicht korrekt - z.B. Title, Interpret, Album sind nominale Datentypen, usw.
*Verwendet bitte bei den Datentypen ein durchgängiges Schema:
** nominal, ordinal, discrete, continuous, binary
** (quantitative kann nämlich discrete oder continuous bedeuten)
*Also entweder haben die Gruppen 9 und 10 von euch diese Tabelle abgekupfert oder umgekehrt? :-/
*Der Datensatz ist als solcher ''multidimensional''
*Subgrouping ist soweit ich weiss bei ID3 tags nicht möglich (ein Genre als Unterpunkt eines anderen) - Woher kommt die Zusatzinformation, die so nicht in den Daten vorkommt?
*IMHO ist die Anzahl verschiedener Genres in einer MP3 Sammlung nicht wirklich "enorm" (ich habe z.B. über 30GB MP3s, aber nur knapp über 100 verschiedene Genres...)
*Für die relativ wenig Information, die in eurer graphischen Repräsentation visualisiert wird, wird sehr viel Platz verwendet. (Ein normaler TreeView mit dazugehängten Balken würde dieselbe Information transportieren, allerdings wesentlich weniger Platz benötigen und damit eine bessere Übersicht ermöglichen.)
*Ihr visualisiert eigentlich nur 2 verschiedene Datenattribute: Genre und Anzahl von Songs innerhalb des Genres. Dafür braucht man eigentlich nicht wirklich besondere, interaktive InfoVis Methoden (die sollten dazu dienen, möglichst viele Parameter übersichtlich und sinnvoll miteinenader zu kombinieren um neues Wissen zu generieren)
*Ihr geht auf die selben Variablen ein wie Gruppe 4 -- der Kopierverdacht erhärtet sich...
*Kurz und gut: überdenkt euer Konzept noch einmal und versucht mehr Information zu integrieren bzw. andere Darstellungsvarianten dafür...

-- [[User:Iwolf|Wolfgang Aigner]] 14:31, 25 November 2005 (CET)

== Kopierverdacht + Korrektur ==

*Bezüglich Ihres Verdachts, dass die Datensatzanalyse abgeschrieben sein könnte, kann ich nur sagen, dass ich diesen Teil selbst erarbeitet habe.
:'''''--> also das halte ich für eine sehr gewagte Theorie und es fällt mir ziemlich schwer das zu glauben... ihr habt die fragliche Tabelle wesentlich später online gestellt und haargenau dieselben Fehler wie die Gruppen 9 und 10 (sogar die Rechtschreibfehler!) - also Zufall hin oder her - 3x die (fast) gleiche Tabelle und identische inhaltliche sowie Rechtschreibfehler...'''''

** --> Rechtschreibfehler konnte ich bei meiner Tabelle keine finden. Zudem haben wir in unserer Angabe als einzige den Datentyp "Quantitative" angegeben. Bei einem Vergleich mit den anderen Gruppen konnte ich ebenfalls Ähnlichkeiten feststellen, die aber wohl eher damit zu begründen sind, dass wir alle das Thema MP3 gewählt haben.

*Der Gründe für die Fehler in meiner Ausarbeitung liegen in folgenden Punkten:
**Leider hatte ich Probleme im Internet (für mich) schlüssige Unterlagen zu diesem Thema finden.
** Zudem waren die Definitionen, die ich zu den diversen Datentypen überhaupt finden konnte oft widersprüchlich.
** So war es für mich schwierig die Daten eindeutig zuzuordnen.
:'''''--> Datentypen sollten eigentlich Grundwissen z.B. aus Statistik sein. Hier nochmal eine kompakte Zusammenfassung: [[Data Type]].'''''

*Bei den Referenzen unserer Ausarbeitung können sie die Internetseite finden, die ich schlussendlich hauptsächlich als Ausgangsbasis für meine Ausarbeitung verwendet habe. Diese Seite stellt eine der detailliertesten Angaben dar, die ich finden konnte.

*Das sich die Fehler in der Analyse zum Teil überdecken, kann ich mir persönlich nur damit erklären, dass die anderen Gruppen mit ähnlichen Schwierigkeiten zu kämpfen hatten.

*Natürlich werden wir unsere Abgabe nochmals überarbeiten bzw. überdenken. Daher möchten wir gerne wissen, bis wann Sie uns hierfür Zeit geben.
:'''''--> Ich erwarte mir eine komplette Überarbeitung eures Konzepts (inkl. Datensatzanalyse) und vor allem einen verbesserten Designentwurf. Eine Deadline für die Überarbeitung gibt es nicht - es ist in eurem Interesse, das so bald wie möglich zu tun, schliesslich ist die Abgabe des Prototypen in weniger als 4 Wochen.'''''

-- [[User:Iwolf|Wolfgang Aigner]] 12:52, 26 November 2005 (CET)

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

2005-11-26T13:52:15Z

UE-InfoVis0506 0026629: /* Kopierverdacht + Korrektur */

'''Kommentar zum Konzept:'''

*Die Datentypen für die Attribute sind nicht korrekt - z.B. Title, Interpret, Album sind nominale Datentypen, usw.
*Verwendet bitte bei den Datentypen ein durchgängiges Schema:
** nominal, ordinal, discrete, continuous, binary
** (quantitative kann nämlich discrete oder continuous bedeuten)
*Also entweder haben die Gruppen 9 und 10 von euch diese Tabelle abgekupfert oder umgekehrt? :-/
*Der Datensatz ist als solcher ''multidimensional''
*Subgrouping ist soweit ich weiss bei ID3 tags nicht möglich (ein Genre als Unterpunkt eines anderen) - Woher kommt die Zusatzinformation, die so nicht in den Daten vorkommt?
*IMHO ist die Anzahl verschiedener Genres in einer MP3 Sammlung nicht wirklich "enorm" (ich habe z.B. über 30GB MP3s, aber nur knapp über 100 verschiedene Genres...)
*Für die relativ wenig Information, die in eurer graphischen Repräsentation visualisiert wird, wird sehr viel Platz verwendet. (Ein normaler TreeView mit dazugehängten Balken würde dieselbe Information transportieren, allerdings wesentlich weniger Platz benötigen und damit eine bessere Übersicht ermöglichen.)
*Ihr visualisiert eigentlich nur 2 verschiedene Datenattribute: Genre und Anzahl von Songs innerhalb des Genres. Dafür braucht man eigentlich nicht wirklich besondere, interaktive InfoVis Methoden (die sollten dazu dienen, möglichst viele Parameter übersichtlich und sinnvoll miteinenader zu kombinieren um neues Wissen zu generieren)
*Ihr geht auf die selben Variablen ein wie Gruppe 4 -- der Kopierverdacht erhärtet sich...
*Kurz und gut: überdenkt euer Konzept noch einmal und versucht mehr Information zu integrieren bzw. andere Darstellungsvarianten dafür...

-- [[User:Iwolf|Wolfgang Aigner]] 14:31, 25 November 2005 (CET)

== Kopierverdacht + Korrektur ==

*Bezüglich Ihres Verdachts, dass die Datensatzanalyse abgeschrieben sein könnte, kann ich nur sagen, dass ich diesen Teil selbst erarbeitet habe.
:'''''--> also das halte ich für eine sehr gewagte Theorie und es fällt mir ziemlich schwer das zu glauben... ihr habt die fragliche Tabelle wesentlich später online gestellt und haargenau dieselben Fehler wie die Gruppen 9 und 10 (sogar die Rechtschreibfehler!) - also Zufall hin oder her - 3x die (fast) gleiche Tabelle und identische inhaltliche sowie Rechtschreibfehler...'''''
** --> Rechtschreibfehler konnte ich bei meiner Tabelle keine finden. Zudem haben wir in unserer Angabe als einzige den Datentyp "Quantitative" angegeben. Bei einem Vergleich mit den anderen Gruppen konnte ich ebenfalls Ähnlichkeiten feststellen, die aber wohl eher damit zu begründen sind, dass wir alle das Thema MP3 gewählt haben.

*Der Gründe für die Fehler in meiner Ausarbeitung liegen in folgenden Punkten:
**Leider hatte ich Probleme im Internet (für mich) schlüssige Unterlagen zu diesem Thema finden.
** Zudem waren die Definitionen, die ich zu den diversen Datentypen überhaupt finden konnte oft widersprüchlich.
** So war es für mich schwierig die Daten eindeutig zuzuordnen.
:'''''--> Datentypen sollten eigentlich Grundwissen z.B. aus Statistik sein. Hier nochmal eine kompakte Zusammenfassung: [[Data Type]].'''''

*Bei den Referenzen unserer Ausarbeitung können sie die Internetseite finden, die ich schlussendlich hauptsächlich als Ausgangsbasis für meine Ausarbeitung verwendet habe. Diese Seite stellt eine der detailliertesten Angaben dar, die ich finden konnte.

*Das sich die Fehler in der Analyse zum Teil überdecken, kann ich mir persönlich nur damit erklären, dass die anderen Gruppen mit ähnlichen Schwierigkeiten zu kämpfen hatten.

*Natürlich werden wir unsere Abgabe nochmals überarbeiten bzw. überdenken. Daher möchten wir gerne wissen, bis wann Sie uns hierfür Zeit geben.
:'''''--> Ich erwarte mir eine komplette Überarbeitung eures Konzepts (inkl. Datensatzanalyse) und vor allem einen verbesserten Designentwurf. Eine Deadline für die Überarbeitung gibt es nicht - es ist in eurem Interesse, das so bald wie möglich zu tun, schliesslich ist die Abgabe des Prototypen in weniger als 4 Wochen.'''''

-- [[User:Iwolf|Wolfgang Aigner]] 12:52, 26 November 2005 (CET)

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

2005-11-25T18:49:12Z

UE-InfoVis0506 0026629: Kopierverdacht + Korrektur

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

2005-11-22T22:28:47Z

UE-InfoVis0506 0026629: /* References */

== 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]]

== 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

2005-11-22T22:28:20Z

UE-InfoVis0506 0026629: /* References */

== 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]]

== 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] MP3, 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

2005-11-22T21:58:22Z

UE-InfoVis0506 0026629: /* Mockup / Fake Screenshot */

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

2005-11-22T21:52:55Z

UE-InfoVis0506 0026629: /* 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, 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 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

2005-11-22T21:49:05Z

UE-InfoVis0506 0026629: /* 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 . 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 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

2005-11-22T21:48:16Z

UE-InfoVis0506 0026629: /* 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. 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 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

2005-11-22T21:38:37Z

UE-InfoVis0506 0026629: /* Dataset Analysis */

== 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 . 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

2005-11-22T21:34:54Z

UE-InfoVis0506 0026629: /* Dataset Analysis */

== 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
|}

== 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

2005-11-22T21:33:57Z

UE-InfoVis0506 0026629: /* Dataset Analysis */

== 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:#FF6600"
!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:#eeeeee"
|Year
|Ordinal
|Year the song was recorded in
|-
|Genre
|Nominal
|Genre the song belongs to
|- style="background:#eeeeee"
|Size
|Quantitative
|Size of the file
|-
|Duration
|Quantitative
|Overall play length of the song
|- style="background:#eeeeee"
|Bit rate
|Ordinal
|Bit rate the file was encoded with
|-
|Sample rate
|Ordinal
|Sample rate the file was encoded with
|- style="background:#eeeeee"
|File Location
|Discrete
|Full system path to the file
|}

== 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

2005-11-22T18:49:26Z

UE-InfoVis0506 0026629: /* Specification of the Application Area and the given Dataset */

== 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 ===

== 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

2005-11-22T18:46:31Z

UE-InfoVis0506 0026629: /* Application area Analysis */

== 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 ===

== 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

2005-11-22T18:45:37Z

UE-InfoVis0506 0026629: /* Special features */

== 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.

== 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

2005-11-22T18:43:01Z

UE-InfoVis0506 0026629: /* Specification of the Application Area and the given Dataset */

== 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.

=== Special features ===

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.

== 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

2005-11-21T16:00:11Z

UE-InfoVis0506 0026629: /* '''Aufgabe 3''' */

== '''Aufgabe 3''' ==

*'''[[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 3 - Design|Design]]'''

*'''[[Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 3 - Prototype|Prototype]]'''

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

2005-11-21T15:59:34Z

UE-InfoVis0506 0026629: /* '''Aufgabe 3''' */

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

2005-11-03T17:56:59Z

UE-InfoVis0506 0026629: /* Modifications: */

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

== Analysis of the Graphic==
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 ink used in the entire display. The goal is to receive the highest possible data-ink ratio (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 elaborate 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 ink 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 repetition 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.

== Correction of the Graphic ==
=== Modifications: ===
* used a line graph instead of a bar graph
: '''+''' The amount of change over the months is now properly visualized.
: '''+''' The data/ink ratio is highly improved.
 
* used visual attributes to highlight certain data.
: '''+''' "SlicerDicers'" is displayed bold to accent it compared to the other products, which is corresponding to the visualized data.
 
* exchanged the gridlines with a grid consisting of a group of slightly coloured fields
: '''+''' Structuring is reduced but a visual aid for reading the graph still remains.
 
* changed the step size of the values on the y-axis.
: '''+''' Unnecessary Structure is removed, which leads to a better data/ink ratio.
 
* changed the style and format of the legend in two different ways.
: '''+''' In both versions we improved the explanation of the data.
: '''+''' In Version 1 we used a split legend to show the information near the corresponding graph.
: '''+''' In Version 2 we placed the labels directly at the corresponding graph.
 
* changed the colours of the graphs using the full colour-spectrum.
: '''+''' Graphs are now much easier to distinguish.
: '''+''' Overall contrast is improved.
 
* changed the text to proper upper- and lowercase.
: '''+''' Text is now spelled correctly.
 
* changed all text to horizontal alignment.
: '''+''' The readability of the information is improved.
 
* removed redundant text from the graphic.
: '''+''' Results in a better data/ink ratio.
 
* removed all unnecessary lines.
: '''+''' Results in a better data/ink ratio.
: '''+''' The readability of the graphic is improved.
 
* removed unnecessary data.
: '''+''' Since the graphic shows a comparison of overall sales, and "RingaDingies" never have a value higher than zero, we removed it from our graphic.
: '''+''' Improves the data/ink ratio.
: '''+''' Improves the readability of the graphic.
 
* added the source and the author of the graphic.
: '''+''' Is important for further use.

== Updated Graphics: ==

[[Image:Slicer_Dicer_Var1.gif|none|image|596|SlicerDicers' sales compared to other products (Version 1)]]
SlicerDicers' sales compared to other products (Version 1)

[[Image:Slicer_Dicer_Var2.gif|none|image|596|SlicerDicers' sales compared to other products (Version 2)]]
SlicerDicers' sales compared to other products (Version 2)

==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

2005-11-03T17:56:43Z

UE-InfoVis0506 0026629: /* Analysis of the Graphic */

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

== Analysis of the Graphic==
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 ink used in the entire display. The goal is to receive the highest possible data-ink ratio (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 elaborate 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 ink 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 repetition 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.

== Correction of the Graphic ==
=== Modifications: ===
* used a line graph instead of a bar graph
: '''+''' The amount of change over the months is now properly visualized.
: '''+''' The data/ink ratio is highly improved.
 
* used visual attributes to highlight certain data.
: '''+''' "SlicerDicers'" is displayed bold to accent it compared to the other products, which is corresponding to the visualized data.
 
* exchanged the gridlines with a grid consisting of a group of slightly coloured fields
: '''+''' Structuring is reduced but a visual aid for reading the graph still remains.
 
* changed the step size of the values on the y-axis.
: '''+''' Unnecessary Structure is removed, which leads to a better data/ink ratio.
 
* changed the style and format of the legend in two different ways.
: '''+''' In both versions we improved the explanation of the data.
: '''+''' In Version 1 we used a split legend to show the information near the corresponding graph.
: '''+''' In Version 2 we placed the labels directly at the corresponding graph.
 
* changed the colours of the graphs using the full colour-spectrum.
: '''+''' Graphs are now much easier to distinguish.
: '''+''' Overall contrast is improved.
 
* changed the text to proper upper- and lowercase.
: '''+''' Text is now spelled correctly.
 
* changed all text to horizontal alignment.
: '''+''' The readability of the informations is improved.
 
* removed redundant text from the graphic.
: '''+''' Results in a better data/ink ratio.
 
* removed all unnecessary lines.
: '''+''' Results in a better data/ink ratio.
: '''+''' The readability of the graphic is improved.
 
* removed unnecessary data.
: '''+''' Since the graphic shows a comparison of overall sales, and "RingaDingies" never have a value higher than zero, we removed it from our graphic.
: '''+''' Improves the data/ink ratio.
: '''+''' Improves the readability of the graphic.
 
* added the source and the author of the graphic.
: '''+''' Is important for further use.

== Updated Graphics: ==

[[Image:Slicer_Dicer_Var1.gif|none|image|596|SlicerDicers' sales compared to other products (Version 1)]]
SlicerDicers' sales compared to other products (Version 1)

[[Image:Slicer_Dicer_Var2.gif|none|image|596|SlicerDicers' sales compared to other products (Version 2)]]
SlicerDicers' sales compared to other products (Version 2)

==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

2005-11-03T16:27:12Z

UE-InfoVis0506 0026629: /* Explanation: */

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

== Analysis of the Graphic==
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 ink used in the entire display. The goal is to receive the highest possible data-ink ratio (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 ink 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.

== Correction of the Graphic ==
=== Modifications: ===
* used a line graph instead of a bar graph
: '''+''' The amount of change over the months is now properly visualized.
: '''+''' The data/ink ratio is highly improved.
 
* used visual attributes to highlight certain data.
: '''+''' "SlicerDicers'" is displayed bold to accent it compared to the other products, which is corresponding to the visualized data.
 
* exchanged the gridlines with a grid consisting of a group of slightly coloured fields
: '''+''' Structuring is reduced but a visual aid for reading the graph still remains.
 
* changed the step size of the values on the y-axis.
: '''+''' Unnecessary Structure is removed, which leads to a better data/ink ratio.
 
* changed the style and format of the legend in two different ways.
: '''+''' In both versions we improved the explanation of the data.
: '''+''' In Version 1 we used a split legend to show the information near the corresponding graph.
: '''+''' In Version 2 we placed the labels directly at the corresponding graph.
 
* changed the colours of the graphs using the full colour-spectrum.
: '''+''' Graphs are now much easier to distinguish.
: '''+''' Overall contrast is improved.
 
* changed the text to proper upper- and lowercase.
: '''+''' Text is now spelled correctly.
 
* changed all text to horizontal alignment.
: '''+''' The readability of the informations is improved.
 
* removed redundant text from the graphic.
: '''+''' Results in a better data/ink ratio.
 
* removed all unnecessary lines.
: '''+''' Results in a better data/ink ratio.
: '''+''' The readability of the graphic is improved.
 
* removed unnecessary data.
: '''+''' Since the graphic shows a comparison of overall sales, and "RingaDingies" never have a value higher than zero, we removed it from our graphic.
: '''+''' Improves the data/ink ratio.
: '''+''' Improves the readability of the graphic.
 
* added the source and the author of the graphic.
: '''+''' Is important for further use.

== Updated Graphics: ==

[[Image:Slicer_Dicer_Var1.gif|none|image|596|SlicerDicers' sales compared to other products (Version 1)]]
SlicerDicers' sales compared to other products (Version 1)

[[Image:Slicer_Dicer_Var2.gif|none|image|596|SlicerDicers' sales compared to other products (Version 2)]]
SlicerDicers' sales compared to other products (Version 2)

==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

2005-11-03T16:27:03Z

UE-InfoVis0506 0026629: /* Modifications: */

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

== Analysis of the Graphic==
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 ink used in the entire display. The goal is to receive the highest possible data-ink ratio (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 ink 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.

== Correction of the Graphic ==
=== Modifications: ===
* used a line graph instead of a bar graph
: '''+''' The amount of change over the months is now properly visualized.
: '''+''' The data/ink ratio is highly improved.
 
* used visual attributes to highlight certain data.
: '''+''' "SlicerDicers'" is displayed bold to accent it compared to the other products, which is corresponding to the visualized data.
 
* exchanged the gridlines with a grid consisting of a group of slightly coloured fields
: '''+''' Structuring is reduced but a visual aid for reading the graph still remains.
 
* changed the step size of the values on the y-axis.
: '''+''' Unnecessary Structure is removed, which leads to a better data/ink ratio.
 
* changed the style and format of the legend in two different ways.
: '''+''' In both versions we improved the explanation of the data.
: '''+''' In Version 1 we used a split legend to show the information near the corresponding graph.
: '''+''' In Version 2 we placed the labels directly at the corresponding graph.
 
* changed the colours of the graphs using the full colour-spectrum.
: '''+''' Graphs are now much easier to distinguish.
: '''+''' Overall contrast is improved.
 
* changed the text to proper upper- and lowercase.
: '''+''' Text is now spelled correctly.
 
* changed all text to horizontal alignment.
: '''+''' The readability of the informations is improved.
 
* removed redundant text from the graphic.
: '''+''' Results in a better data/ink ratio.
 
* removed all unnecessary lines.
: '''+''' Results in a better data/ink ratio.
: '''+''' The readability of the graphic is improved.
 
* removed unnecessary data.
: '''+''' Since the graphic shows a comparison of overall sales, and "RingaDingies" never have a value higher than zero, we removed it from our graphic.
: '''+''' Improves the data/ink ratio.
: '''+''' Improves the readability of the graphic.
 
* added the source and the author of the graphic.
: '''+''' Is important for further use.

=== Explanation: ===

Due to the information represented in the graphic, we decided to use a line graph instead of a bar graph. This way the amount of change over the months is properly visualized.

== Updated Graphics: ==

[[Image:Slicer_Dicer_Var1.gif|none|image|596|SlicerDicers' sales compared to other products (Version 1)]]
SlicerDicers' sales compared to other products (Version 1)

[[Image:Slicer_Dicer_Var2.gif|none|image|596|SlicerDicers' sales compared to other products (Version 2)]]
SlicerDicers' sales compared to other products (Version 2)

==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

2005-11-03T14:49:18Z

UE-InfoVis0506 0026629: /* Explanation: */

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

== Analysis of the Graphic==
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 ink used in the entire display. The goal is to receive the highest possible data-ink ratio (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 ink 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.

== Correction of the Graphic ==
=== Modifications: ===
* used a line graph instead of a bar graph
* used visual attributes to highlight certain data.
* exchanged the gridlines with a grid consisting of a group of slightly coloured fields
* changed the step size of the values on the y-axis.
* changed the style and format of the legend in two different ways.
* changed the colours of the graphs using the full colour-spectrum.
* changed the text to proper upper- and lowercase.
* changed all text to horizontal alignment.
* removed redundant text from the graphic.
* removed all unnecessary lines.
* removed unnecessary data.
* added the source and the author of the graphic.

=== Explanation: ===

Due to the information represented in the graphic, we decided to use a line graph instead of a bar graph. This way the amount of change over the months is properly visualized.

== Updated Graphics: ==

[[Image:Slicer_Dicer_Var1.gif|none|image|596|SlicerDicers' sales compared to other products (Version 1)]]
SlicerDicers' sales compared to other products (Version 1)

[[Image:Slicer_Dicer_Var2.gif|none|image|596|SlicerDicers' sales compared to other products (Version 2)]]
SlicerDicers' sales compared to other products (Version 2)

==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

2005-11-02T14:05:55Z

UE-InfoVis0506 0026629: /* Updated Graphics: */

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

2005-11-02T14:05:30Z

UE-InfoVis0506 0026629: /* Updated Graphics: */

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

2005-11-02T14:00:54Z

UE-InfoVis0506 0026629: /* Modifications: */

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

2005-11-02T11:35:10Z

UE-InfoVis0506 0026629: /* Updated Graphics: */

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

2005-11-02T11:33:57Z

UE-InfoVis0506 0026629: /* Updated Graphics: */

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

2005-11-02T11:31:09Z

UE-InfoVis0506 0026629: /* Updated Graphics: */

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

2005-11-02T11:30:20Z

UE-InfoVis0506 0026629: /* Modifications: */

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

2005-11-02T11:05:36Z

UE-InfoVis0506 0026629: /* Poor Graphic */

Teaching talk:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 1

2005-11-02T10:58:15Z

UE-InfoVis0506 0026629: /* Korrektur "Lie Factor" */

== Bewertung ==
'''Punkte: 12'''

'''Begründung:'''
*Formatierung für Literaturverweise nicht eingehalten
*Zitate nicht richtig formatiert / Formatierungstemplates nicht verwendet
*Quellenangaben bei Bildern fehlen (auf Image page)
*Formatierung der Quellenangaben bei Bildern fehlerhaft
*'''teilweise fehlende Quellenangaben!'''
*kleiner Fehler bei Literaturangaben (Ziffern statt richtiger Formatierung)
*Bezug zu Designdisziplinen fehlt (bei Ockham's Razor)

Ihr habt die Möglichkeit, eure Ausarbeitung bis 3.11.2005 zu überarbeiten, um die derzeitigen Unzulänglichkeiten auszubessern. Wird das gemacht, so werden die Punkte danach klarerweise aufgewertet.

-- [[User:Iwolf|Wolfgang Aigner]] 17:59, 26 October 2005 (CEST)

== Korrektur "Lie Factor" ==

*Formatierung für Literaturverweise nicht eingehalten
** Formatierung überprüft, keine Fehler gefunden.
:''--> passt jetzt''

*Zitate nicht richtig formatiert / Formatierungstemplates nicht verwendet
** Zitatverweise ausgebessert (nicht mehr kursiv)
:''--> OK''

*Quellenangaben bei Bildern fehlen (auf Image page)
** Formeln sind selbst gemacht, daher keine Quellenangabe.
** restliche Bilder ergänzt.
:'''''--> Quellenangaben nicht richtig formatiert (Format wie oben)'''''
::* Ausgebessert. Formatierung aus unserem Artikel übernommen.
*Formatierung der Quellenangaben bei Bildern fehlerhaft
** ausgebessert (Ziffern ersetzt)

*'''teilweise fehlende Quellenangaben!'''
** keine fehlenden Quellen gefunden.
:'''''--> die Texte bei den "Examples" sind nicht von euch, sondern von hier: http://www.math.yorku.ca/SCS/Gallery/lie-factor.html'''''
::* Ergänzt.
::'''''--> Zitat nicht richtig formatiert (wo fängt es an, wo hört es auf?)''''' ([[User:Iwolf|Wolfgang Aigner]] 08:31, 2 November 2005 (CET))
:::* '''Erneut Korrigiert''' ( [[User:UE-InfoVis0506 0026629|Feierteifl]] 11:58, 2 November 2005 (CET) )
** Grafiken der Formeln sind selbst erstellt.

*kleiner Fehler bei Literaturangaben (Ziffern statt richtiger Formatierung)
** ausgebessert
:''--> passt''

Falls noch Fehler vorhanden sind, so kann ich sie nicht entdecken, bitte um genauere Fehlerspezifikation.
:''--> siehe oben ([[User:Iwolf|Wolfgang Aigner]] 08:28, 28 October 2005 (CEST))''

Lie Factor

2005-11-02T10:53:39Z

UE-InfoVis0506 0026629: /* Example 1: */

==The Lie Factor==

=== Definition ===

The “Lie Factor” is a value to describe the relation between the size of effect shown in a graphic and the size of effect shown in the data.

Edward Tufte, Prof. at the Yale University, defined the “Lie Factor” in his book “The Visual Display of Quantitative Information” in 1983.

He states the principle that ''"The representation of numbers, as physically measured on the surface of the graphic itself, should be directly proportional to the quantities represented."'' [Tufte, 1991]

This results in the following formula:

[[Image:lie_factor_formel.jpg|Formula for calculating the Lie Factor]]

where

[[Image:lie_factor_formel_size_of_effect.jpg|Formula for calculating the size of effect]]

In other words, the Lie Factor is ''“the size of an effect shown in a graph divided by the actual size of the effect in the data on which the graph is based”.'' [Craven, 2001]

===Description===

To ensure the Integrity of a graphic, its Lie Factor should have a value between 0.95 and 1.05. If the value is less or greater, it indicates a substantial (and often intended) distortion, far beyond minor inaccuracies (e.g. caused by plotting).

For example, if the lie factor had the value 1.02, then the graphic would not be described as a “lying” one, since a jitter in the graphic or another minor error is more likely the cause for the given distortion. On the other hand, if the factor had the value 1.45, we can generally assume that the producer intentionally distorted the graphic.
Is the Lie Factor greater than 1, the concerned graphic is so called “Overstating”. Is it less than 1, the graphic is “Understating”.

----

==Examples:==

===Example 1:===

This graphic was originally published by the NY Times. It tries to show the mandated fuel economy standards for autos set by the US Department of Transportation. [Friendly, 2005]

[[Image:lie_factor_example1_image.jpg|Example1 for the Lie Factor]]

:::::::::::::::::::[Tufte, 1991]

''The standard required an increase in mileage from 18 to 27.5, an increase of 53%. The magnitude of increase shown in the graph is 783%, which results in a lie factor of 14.8!'' [Friendly, 2005]

The following formula shows the exact calculation:

[[Image:lie_factor_example1.jpg|Example1 for the Lie Factor]]

===Example 2:===

[[Image:lie_factor_example2.jpg|Example2 for the Lie Factor]]

::::::::[Tufte, 1991]

An important point for producing informative graphs, is the avoidance of ''confounding design variation with data variation'' [Friendly, 2005]. In other words, ''the scale of the graphic should always correspond to changes in the data being represented'' [Friendly, 2005]. The graph shown in this example breaks that rule ''by using area to show one-dimensional data, giving a lie factor = 2.8'' [Friendly, 2005].
----

==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

[Galliat, 2005] Tobias Galliat , Visualisierung von Informationsräumen, Fachhochschule Köln, University of Applied Sciences Cologne, Created at: March 30, 2005, Retrieved at: October 24, 2005, http://www.fbi.fh-koeln.de/institut/personen/galliat/Material/ss05/Visualisierung_von_Informationsraeumen_V2_2005.pdf

[Tufte, 1991] Edward Tufte, The Visual Display of Quantitative Information, Second Edition, Graphics Press, USA, 1991, p. 57 – 69.

[Mueller, 2005] Klaus Mueller, Introduction to Visualization, Computer Science Department, Stony Brook University, Created at: September 6, 2003, Retrieved at: October 24, 2005, http://www.cs.sunysb.edu/~mueller/teaching/cse332/tufte.pdf

[Ihaka, 2003] Ross Ihaka, Good and Bad Graphs, Department of Statistics, University of Auckland, Created at: October 16, 2003, Retrieved at: October 24, 2005, http://www.stat.auckland.ac.nz/~ihaka/120/Lectures/lecture03-8up.pdf

[Kwc Blog, 2003] Kwc Blog, About Book: Visual Display of Quantitative Information, Created at: May 28, 2003, Retrieved at: October 24, 2005, http://kwc.org/blog/archives/2003/2003-05-28.book_visual_display_of_quantitative_information.html

[Craven, 2001b] Tim Craven, LIS 504 - Graphic displays of data, Faculty of Information and Media Studies at the University of Western Ontario, Last updated: November 6, 2000, Retrieved at: October 24, 2005, http://instruct.uwo.ca/fim-lis/504/504gra.htm

[Friendly, 2005] Michael Friendly, The Lie Factor, Department of Mathematics and Statistics, Toronto, Last updated: February 23, 2002, Retrieved at: October 24, 2005, http://www.math.yorku.ca/SCS/Gallery/lie-factor.html

Lie Factor

2005-11-02T10:52:59Z

UE-InfoVis0506 0026629: /* Example 2: */

==The Lie Factor==

=== Definition ===

The “Lie Factor” is a value to describe the relation between the size of effect shown in a graphic and the size of effect shown in the data.

Edward Tufte, Prof. at the Yale University, defined the “Lie Factor” in his book “The Visual Display of Quantitative Information” in 1983.

He states the principle that ''"The representation of numbers, as physically measured on the surface of the graphic itself, should be directly proportional to the quantities represented."'' [Tufte, 1991]

This results in the following formula:

[[Image:lie_factor_formel.jpg|Formula for calculating the Lie Factor]]

where

[[Image:lie_factor_formel_size_of_effect.jpg|Formula for calculating the size of effect]]

In other words, the Lie Factor is ''“the size of an effect shown in a graph divided by the actual size of the effect in the data on which the graph is based”.'' [Craven, 2001]

===Description===

To ensure the Integrity of a graphic, its Lie Factor should have a value between 0.95 and 1.05. If the value is less or greater, it indicates a substantial (and often intended) distortion, far beyond minor inaccuracies (e.g. caused by plotting).

For example, if the lie factor had the value 1.02, then the graphic would not be described as a “lying” one, since a jitter in the graphic or another minor error is more likely the cause for the given distortion. On the other hand, if the factor had the value 1.45, we can generally assume that the producer intentionally distorted the graphic.
Is the Lie Factor greater than 1, the concerned graphic is so called “Overstating”. Is it less than 1, the graphic is “Understating”.

----

==Examples:==

===Example 1:===

This graphic was originally published by the NY Times. It tries to show the mandated fuel economy standars for autos set by the US Department of Transportation. [Friendly, 2005]

[[Image:lie_factor_example1_image.jpg|Example1 for the Lie Factor]]

:::::::::::::::::::[Tufte, 1991]

''The standard required an increase in mileage from 18 to 27.5, an increase of 53%. The magnitude of increase shown in the graph is 783%, which results in a lie factor of 14.8!'' [Friendly, 2005]

The following formula shows the exact calculation:

[[Image:lie_factor_example1.jpg|Example1 for the Lie Factor]]

===Example 2:===

[[Image:lie_factor_example2.jpg|Example2 for the Lie Factor]]

::::::::[Tufte, 1991]

An important point for producing informative graphs, is the avoidance of ''confounding design variation with data variation'' [Friendly, 2005]. In other words, ''the scale of the graphic should always correspond to changes in the data being represented'' [Friendly, 2005]. The graph shown in this example breaks that rule ''by using area to show one-dimensional data, giving a lie factor = 2.8'' [Friendly, 2005].
----

==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

[Galliat, 2005] Tobias Galliat , Visualisierung von Informationsräumen, Fachhochschule Köln, University of Applied Sciences Cologne, Created at: March 30, 2005, Retrieved at: October 24, 2005, http://www.fbi.fh-koeln.de/institut/personen/galliat/Material/ss05/Visualisierung_von_Informationsraeumen_V2_2005.pdf

[Tufte, 1991] Edward Tufte, The Visual Display of Quantitative Information, Second Edition, Graphics Press, USA, 1991, p. 57 – 69.

[Mueller, 2005] Klaus Mueller, Introduction to Visualization, Computer Science Department, Stony Brook University, Created at: September 6, 2003, Retrieved at: October 24, 2005, http://www.cs.sunysb.edu/~mueller/teaching/cse332/tufte.pdf

[Ihaka, 2003] Ross Ihaka, Good and Bad Graphs, Department of Statistics, University of Auckland, Created at: October 16, 2003, Retrieved at: October 24, 2005, http://www.stat.auckland.ac.nz/~ihaka/120/Lectures/lecture03-8up.pdf

[Kwc Blog, 2003] Kwc Blog, About Book: Visual Display of Quantitative Information, Created at: May 28, 2003, Retrieved at: October 24, 2005, http://kwc.org/blog/archives/2003/2003-05-28.book_visual_display_of_quantitative_information.html

[Craven, 2001b] Tim Craven, LIS 504 - Graphic displays of data, Faculty of Information and Media Studies at the University of Western Ontario, Last updated: November 6, 2000, Retrieved at: October 24, 2005, http://instruct.uwo.ca/fim-lis/504/504gra.htm

[Friendly, 2005] Michael Friendly, The Lie Factor, Department of Mathematics and Statistics, Toronto, Last updated: February 23, 2002, Retrieved at: October 24, 2005, http://www.math.yorku.ca/SCS/Gallery/lie-factor.html

Lie Factor

2005-11-02T10:43:40Z

UE-InfoVis0506 0026629: /* Example 1: */

==The Lie Factor==

=== Definition ===

The “Lie Factor” is a value to describe the relation between the size of effect shown in a graphic and the size of effect shown in the data.

Edward Tufte, Prof. at the Yale University, defined the “Lie Factor” in his book “The Visual Display of Quantitative Information” in 1983.

He states the principle that ''"The representation of numbers, as physically measured on the surface of the graphic itself, should be directly proportional to the quantities represented."'' [Tufte, 1991]

This results in the following formula:

[[Image:lie_factor_formel.jpg|Formula for calculating the Lie Factor]]

where

[[Image:lie_factor_formel_size_of_effect.jpg|Formula for calculating the size of effect]]

In other words, the Lie Factor is ''“the size of an effect shown in a graph divided by the actual size of the effect in the data on which the graph is based”.'' [Craven, 2001]

===Description===

To ensure the Integrity of a graphic, its Lie Factor should have a value between 0.95 and 1.05. If the value is less or greater, it indicates a substantial (and often intended) distortion, far beyond minor inaccuracies (e.g. caused by plotting).

For example, if the lie factor had the value 1.02, then the graphic would not be described as a “lying” one, since a jitter in the graphic or another minor error is more likely the cause for the given distortion. On the other hand, if the factor had the value 1.45, we can generally assume that the producer intentionally distorted the graphic.
Is the Lie Factor greater than 1, the concerned graphic is so called “Overstating”. Is it less than 1, the graphic is “Understating”.

----

==Examples:==

===Example 1:===

This graphic was originally published by the NY Times. It tries to show the mandated fuel economy standars for autos set by the US Department of Transportation. [Friendly, 2005]

[[Image:lie_factor_example1_image.jpg|Example1 for the Lie Factor]]

:::::::::::::::::::[Tufte, 1991]

''The standard required an increase in mileage from 18 to 27.5, an increase of 53%. The magnitude of increase shown in the graph is 783%, which results in a lie factor of 14.8!'' [Friendly, 2005]

The following formula shows the exact calculation:

[[Image:lie_factor_example1.jpg|Example1 for the Lie Factor]]

===Example 2:===

[[Image:lie_factor_example2.jpg|Example2 for the Lie Factor]]

::::::::[Tufte, 1991]

Another key element in making informative graphs is to avoid confounding design variation with data variation. This means that changes in the scale of the graphic should always correspond to changes in the data being represented. This graph violates that principle by using area to show one-dimensional data, giving a lie factor = 2.8. [Friendly, 2005]

----

==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

[Galliat, 2005] Tobias Galliat , Visualisierung von Informationsräumen, Fachhochschule Köln, University of Applied Sciences Cologne, Created at: March 30, 2005, Retrieved at: October 24, 2005, http://www.fbi.fh-koeln.de/institut/personen/galliat/Material/ss05/Visualisierung_von_Informationsraeumen_V2_2005.pdf

[Tufte, 1991] Edward Tufte, The Visual Display of Quantitative Information, Second Edition, Graphics Press, USA, 1991, p. 57 – 69.

[Mueller, 2005] Klaus Mueller, Introduction to Visualization, Computer Science Department, Stony Brook University, Created at: September 6, 2003, Retrieved at: October 24, 2005, http://www.cs.sunysb.edu/~mueller/teaching/cse332/tufte.pdf

[Ihaka, 2003] Ross Ihaka, Good and Bad Graphs, Department of Statistics, University of Auckland, Created at: October 16, 2003, Retrieved at: October 24, 2005, http://www.stat.auckland.ac.nz/~ihaka/120/Lectures/lecture03-8up.pdf

[Kwc Blog, 2003] Kwc Blog, About Book: Visual Display of Quantitative Information, Created at: May 28, 2003, Retrieved at: October 24, 2005, http://kwc.org/blog/archives/2003/2003-05-28.book_visual_display_of_quantitative_information.html

[Craven, 2001b] Tim Craven, LIS 504 - Graphic displays of data, Faculty of Information and Media Studies at the University of Western Ontario, Last updated: November 6, 2000, Retrieved at: October 24, 2005, http://instruct.uwo.ca/fim-lis/504/504gra.htm

[Friendly, 2005] Michael Friendly, The Lie Factor, Department of Mathematics and Statistics, Toronto, Last updated: February 23, 2002, Retrieved at: October 24, 2005, http://www.math.yorku.ca/SCS/Gallery/lie-factor.html

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

2005-11-01T17:43:55Z

UE-InfoVis0506 0026629: /* 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 ink used in the entire display. The goal is to receive the highest possible data-ink ratio (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 ink 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 talk:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 1

2005-10-28T12:49:05Z

UE-InfoVis0506 0026629: /* Korrektur "Lie Factor" */

== Bewertung ==
'''Punkte: 12'''

'''Begründung:'''
*Formatierung für Literaturverweise nicht eingehalten
*Zitate nicht richtig formatiert / Formatierungstemplates nicht verwendet
*Quellenangaben bei Bildern fehlen (auf Image page)
*Formatierung der Quellenangaben bei Bildern fehlerhaft
*'''teilweise fehlende Quellenangaben!'''
*kleiner Fehler bei Literaturangaben (Ziffern statt richtiger Formatierung)
*Bezug zu Designdisziplinen fehlt (bei Ockham's Razor)

Ihr habt die Möglichkeit, eure Ausarbeitung bis 3.11.2005 zu überarbeiten, um die derzeitigen Unzulänglichkeiten auszubessern. Wird das gemacht, so werden die Punkte danach klarerweise aufgewertet.

-- [[User:Iwolf|Wolfgang Aigner]] 17:59, 26 October 2005 (CEST)

== Korrektur "Lie Factor" ==

*Formatierung für Literaturverweise nicht eingehalten
** Formatierung überprüft, keine Fehler gefunden.
:''--> passt jetzt''

*Zitate nicht richtig formatiert / Formatierungstemplates nicht verwendet
** Zitatverweise ausgebessert (nicht mehr kursiv)
:''--> OK''

*Quellenangaben bei Bildern fehlen (auf Image page)
** Formeln sind selbst gemacht, daher keine Quellenangabe.
** restliche Bilder ergänzt.
:'''''--> Quellenangaben nicht richtig formatiert (Format wie oben)'''''
::* Ausgebessert. Formatierung aus unserem Artikel übernommen.
*Formatierung der Quellenangaben bei Bildern fehlerhaft
** ausgebessert (Ziffern ersetzt)

*'''teilweise fehlende Quellenangaben!'''
** keine fehlenden Quellen gefunden.
:'''''--> die Texte bei den "Examples" sind nicht von euch, sondern von hier: http://www.math.yorku.ca/SCS/Gallery/lie-factor.html'''''
::* Ergänzt.
** Grafiken der Formeln sind selbst erstellt.

*kleiner Fehler bei Literaturangaben (Ziffern statt richtiger Formatierung)
** ausgebessert
:''--> passt''

Falls noch Fehler vorhanden sind, so kann ich sie nicht entdecken, bitte um genauere Fehlerspezifikation.
:''--> siehe oben ([[User:Iwolf|Wolfgang Aigner]] 08:28, 28 October 2005 (CEST))''

Teaching talk:TUW - UE InfoVis WS 2005/06 - Gruppe G4 - Aufgabe 1

2005-10-28T12:48:56Z

UE-InfoVis0506 0026629: /* Korrektur "Lie Factor" */

== Bewertung ==
'''Punkte: 12'''

'''Begründung:'''
*Formatierung für Literaturverweise nicht eingehalten
*Zitate nicht richtig formatiert / Formatierungstemplates nicht verwendet
*Quellenangaben bei Bildern fehlen (auf Image page)
*Formatierung der Quellenangaben bei Bildern fehlerhaft
*'''teilweise fehlende Quellenangaben!'''
*kleiner Fehler bei Literaturangaben (Ziffern statt richtiger Formatierung)
*Bezug zu Designdisziplinen fehlt (bei Ockham's Razor)

Ihr habt die Möglichkeit, eure Ausarbeitung bis 3.11.2005 zu überarbeiten, um die derzeitigen Unzulänglichkeiten auszubessern. Wird das gemacht, so werden die Punkte danach klarerweise aufgewertet.

-- [[User:Iwolf|Wolfgang Aigner]] 17:59, 26 October 2005 (CEST)

== Korrektur "Lie Factor" ==

*Formatierung für Literaturverweise nicht eingehalten
** Formatierung überprüft, keine Fehler gefunden.
:''--> passt jetzt''

*Zitate nicht richtig formatiert / Formatierungstemplates nicht verwendet
** Zitatverweise ausgebessert (nicht mehr kursiv)
:''--> OK''

*Quellenangaben bei Bildern fehlen (auf Image page)
** Formeln sind selbst gemacht, daher keine Quellenangabe.
** restliche Bilder ergänzt.
:'''''--> Quellenangaben nicht richtig formatiert (Format wie oben)'''''
::* Ausgebessert. Formatierung aus unserem Artikel übernommen.
*Formatierung der Quellenangaben bei Bildern fehlerhaft
** ausgebessert (Ziffern ersetzt)

*'''teilweise fehlende Quellenangaben!'''
** keine fehlenden Quellen gefunden.
:'''''--> die Texte bei den "Examples" sind nicht von euch, sondern von hier: http://www.math.yorku.ca/SCS/Gallery/lie-factor.html'''''
:* Ergänzt.
** Grafiken der Formeln sind selbst erstellt.

*kleiner Fehler bei Literaturangaben (Ziffern statt richtiger Formatierung)
** ausgebessert
:''--> passt''

Falls noch Fehler vorhanden sind, so kann ich sie nicht entdecken, bitte um genauere Fehlerspezifikation.
:''--> siehe oben ([[User:Iwolf|Wolfgang Aigner]] 08:28, 28 October 2005 (CEST))''

Lie Factor

2005-10-28T12:44:53Z

UE-InfoVis0506 0026629: /* Example 1: */

==The Lie Factor==

=== Definition ===

The “Lie Factor” is a value to describe the relation between the size of effect shown in a graphic and the size of effect shown in the data.

Edward Tufte, Prof. at the Yale University, defined the “Lie Factor” in his book “The Visual Display of Quantitative Information” in 1983.

He states the principle that ''"The representation of numbers, as physically measured on the surface of the graphic itself, should be directly proportional to the quantities represented."'' [Tufte, 1991]

This results in the following formula:

[[Image:lie_factor_formel.jpg|Formula for calculating the Lie Factor]]

where

[[Image:lie_factor_formel_size_of_effect.jpg|Formula for calculating the size of effect]]

In other words, the Lie Factor is ''“the size of an effect shown in a graph divided by the actual size of the effect in the data on which the graph is based”.'' [Craven, 2001]

===Description===

To ensure the Integrity of a graphic, its Lie Factor should have a value between 0.95 and 1.05. If the value is less or greater, it indicates a substantial (and often intended) distortion, far beyond minor inaccuracies (e.g. caused by plotting).

For example, if the lie factor had the value 1.02, then the graphic would not be described as a “lying” one, since a jitter in the graphic or another minor error is more likely the cause for the given distortion. On the other hand, if the factor had the value 1.45, we can generally assume that the producer intentionally distorted the graphic.
Is the Lie Factor greater than 1, the concerned graphic is so called “Overstating”. Is it less than 1, the graphic is “Understating”.

----

==Examples:==

===Example 1:===

This graph, from the NY Times, purports to show the mandated fuel economy standards set by the US Department of Transportation.

[[Image:lie_factor_example1_image.jpg|Example1 for the Lie Factor]]

:::::::::::::::::::[Tufte, 1991]

The standard required an increase in mileage from 18 to 27.5, an increase of 53%. The magnitude of increase shown in the graph is 783%, which results in a lie factor of 14.8! [Friendly, 2005]

The following formula shows the exact calculation:

[[Image:lie_factor_example1.jpg|Example1 for the Lie Factor]]

===Example 2:===

[[Image:lie_factor_example2.jpg|Example2 for the Lie Factor]]

::::::::[Tufte, 1991]

Another key element in making informative graphs is to avoid confounding design variation with data variation. This means that changes in the scale of the graphic should always correspond to changes in the data being represented. This graph violates that principle by using area to show one-dimensional data, giving a lie factor = 2.8. [Friendly, 2005]

----

==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

[Galliat, 2005] Tobias Galliat , Visualisierung von Informationsräumen, Fachhochschule Köln, University of Applied Sciences Cologne, Created at: March 30, 2005, Retrieved at: October 24, 2005, http://www.fbi.fh-koeln.de/institut/personen/galliat/Material/ss05/Visualisierung_von_Informationsraeumen_V2_2005.pdf

[Tufte, 1991] Edward Tufte, The Visual Display of Quantitative Information, Second Edition, Graphics Press, USA, 1991, p. 57 – 69.

[Mueller, 2005] Klaus Mueller, Introduction to Visualization, Computer Science Department, Stony Brook University, Created at: September 6, 2003, Retrieved at: October 24, 2005, http://www.cs.sunysb.edu/~mueller/teaching/cse332/tufte.pdf

[Ihaka, 2003] Ross Ihaka, Good and Bad Graphs, Department of Statistics, University of Auckland, Created at: October 16, 2003, Retrieved at: October 24, 2005, http://www.stat.auckland.ac.nz/~ihaka/120/Lectures/lecture03-8up.pdf

[Kwc Blog, 2003] Kwc Blog, About Book: Visual Display of Quantitative Information, Created at: May 28, 2003, Retrieved at: October 24, 2005, http://kwc.org/blog/archives/2003/2003-05-28.book_visual_display_of_quantitative_information.html

[Craven, 2001b] Tim Craven, LIS 504 - Graphic displays of data, Faculty of Information and Media Studies at the University of Western Ontario, Last updated: November 6, 2000, Retrieved at: October 24, 2005, http://instruct.uwo.ca/fim-lis/504/504gra.htm

[Friendly, 2005] Michael Friendly, The Lie Factor, Department of Mathematics and Statistics, Toronto, Last updated: February 23, 2002, Retrieved at: October 24, 2005, http://www.math.yorku.ca/SCS/Gallery/lie-factor.html

Lie Factor

2005-10-28T12:44:44Z

UE-InfoVis0506 0026629: /* Example 2: */

==The Lie Factor==

=== Definition ===

The “Lie Factor” is a value to describe the relation between the size of effect shown in a graphic and the size of effect shown in the data.

Edward Tufte, Prof. at the Yale University, defined the “Lie Factor” in his book “The Visual Display of Quantitative Information” in 1983.

He states the principle that ''"The representation of numbers, as physically measured on the surface of the graphic itself, should be directly proportional to the quantities represented."'' [Tufte, 1991]

This results in the following formula:

[[Image:lie_factor_formel.jpg|Formula for calculating the Lie Factor]]

where

[[Image:lie_factor_formel_size_of_effect.jpg|Formula for calculating the size of effect]]

In other words, the Lie Factor is ''“the size of an effect shown in a graph divided by the actual size of the effect in the data on which the graph is based”.'' [Craven, 2001]

===Description===

To ensure the Integrity of a graphic, its Lie Factor should have a value between 0.95 and 1.05. If the value is less or greater, it indicates a substantial (and often intended) distortion, far beyond minor inaccuracies (e.g. caused by plotting).

For example, if the lie factor had the value 1.02, then the graphic would not be described as a “lying” one, since a jitter in the graphic or another minor error is more likely the cause for the given distortion. On the other hand, if the factor had the value 1.45, we can generally assume that the producer intentionally distorted the graphic.
Is the Lie Factor greater than 1, the concerned graphic is so called “Overstating”. Is it less than 1, the graphic is “Understating”.

----

==Examples:==

===Example 1:===

This graph, from the NY Times, purports to show the mandated fuel economy standards set by the US Department of Transportation.

[[Image:lie_factor_example1_image.jpg|Example1 for the Lie Factor]]

:::::::::::::::::::[Tufte, 1991]

The standard required an increase in mileage from 18 to 27.5, an increase of 53%. The magnitude of increase shown in the graph is 783%, which results in a lie factor of 14.8!

The following formula shows the exact calculation:

[[Image:lie_factor_example1.jpg|Example1 for the Lie Factor]]

===Example 2:===

[[Image:lie_factor_example2.jpg|Example2 for the Lie Factor]]

::::::::[Tufte, 1991]

Another key element in making informative graphs is to avoid confounding design variation with data variation. This means that changes in the scale of the graphic should always correspond to changes in the data being represented. This graph violates that principle by using area to show one-dimensional data, giving a lie factor = 2.8. [Friendly, 2005]

----

==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

[Galliat, 2005] Tobias Galliat , Visualisierung von Informationsräumen, Fachhochschule Köln, University of Applied Sciences Cologne, Created at: March 30, 2005, Retrieved at: October 24, 2005, http://www.fbi.fh-koeln.de/institut/personen/galliat/Material/ss05/Visualisierung_von_Informationsraeumen_V2_2005.pdf

[Tufte, 1991] Edward Tufte, The Visual Display of Quantitative Information, Second Edition, Graphics Press, USA, 1991, p. 57 – 69.

[Mueller, 2005] Klaus Mueller, Introduction to Visualization, Computer Science Department, Stony Brook University, Created at: September 6, 2003, Retrieved at: October 24, 2005, http://www.cs.sunysb.edu/~mueller/teaching/cse332/tufte.pdf

[Ihaka, 2003] Ross Ihaka, Good and Bad Graphs, Department of Statistics, University of Auckland, Created at: October 16, 2003, Retrieved at: October 24, 2005, http://www.stat.auckland.ac.nz/~ihaka/120/Lectures/lecture03-8up.pdf

[Kwc Blog, 2003] Kwc Blog, About Book: Visual Display of Quantitative Information, Created at: May 28, 2003, Retrieved at: October 24, 2005, http://kwc.org/blog/archives/2003/2003-05-28.book_visual_display_of_quantitative_information.html

[Craven, 2001b] Tim Craven, LIS 504 - Graphic displays of data, Faculty of Information and Media Studies at the University of Western Ontario, Last updated: November 6, 2000, Retrieved at: October 24, 2005, http://instruct.uwo.ca/fim-lis/504/504gra.htm

[Friendly, 2005] Michael Friendly, The Lie Factor, Department of Mathematics and Statistics, Toronto, Last updated: February 23, 2002, Retrieved at: October 24, 2005, http://www.math.yorku.ca/SCS/Gallery/lie-factor.html

File:Lie factor example2.jpg

2005-10-28T12:43:53Z

UE-InfoVis0506 0026629: /* Quelle */

== Beschreibung ==
Bild zu Beispiel zum Thema "Lie Factor"
== Copyright-Status ==

== Quelle ==
[Tufte, 1991] Edward Tufte, The Visual Display of Quantitative Information, Second Edition, Graphics Press, USA, 1991, p. 69.

File:Lie factor example1 image.jpg

2005-10-28T12:43:10Z

UE-InfoVis0506 0026629: /* Quelle */

File:Lie factor formel size of effect.jpg

2005-10-27T12:59:06Z

UE-InfoVis0506 0026629: /* Quelle */

== Beschreibung ==
Teilformel zum berechnen des "Lie Factor"
== Copyright-Status ==

== Quelle ==
User: UE-InfoVis0506 0026629