Teaching:TUW - UE InfoVis WS 2005/06 - Gruppe 10 - Aufgabe 3 - Design
Application Area and given Dataset
Application Area Analysis
The application area for this task is to visualize a music archive with the data provided by ID3/iTunes tags. These tags include information about songname, artist, year, album,... The challenge in visualizing this information is to select "good" attributes that can be brought into relation, because of the high number of discreet and "organizational" attributes.
Dataset Analysis
Attribute | Data type | Description |
---|---|---|
Name | Discreet | Song title |
Artist | Discreet | Artist name |
Album | Discreet | Album name |
Genre | Nominal | Genre the song belongs to |
Composer | Discreet | The composer of the song |
Size | Ordinal | The file size |
Total time | Ordinal | The total time of the song |
Disc number | Ordinal | Number of the disc |
Disc count | Ordinal | Total number of discs |
Track number | Ordinal | Track number of this song on the disc |
Track count | Ordinal | Total number of tracks on the disc |
Year | Ordinal | Year of origin |
Date Modified | Ordinal | Date of modification |
Date added | Ordinal | Date when song was added to archive |
Bit rate | Ordinal | Bit rate of song (e.g. 128kbit/s) |
Sample rate | Ordinal | Sample rate of song (e.g. 44100Hz) |
Play count | Ordinal | Number of time the song was played |
Play date | Ordinal | Date of last play |
Play date UTC | Ordinal | Date of last play in UTC |
Location | Discreet | File location |
File Folder Count | Ordinal | Number of file in the same folder as the current song |
Library folder count | Ordinal | Number of files in library folder |
Kind | Nominal | Kind of file (e.g. MPEG audio file) |
Rating | Ordinal | Personal rating (1-5 stars) |
One data record consists of a concatenation of attributes listed in the table above. Those attributes are all 1-dimensional. A data record contains only necessary attributes or attributes with data.
Target Group Analysis
Who should use this visualation technique?
The visualisation is mainly meant for people collecting mp3s or for musicians who like to see some nice information they won't find in the usual representations. Moreover it's for people who like to explore their music collection under different standpoints. This will fit the exploring character of information visualisation.
Our visualisation tool will make it possible to browse through the music collection in different ways than what iTunes offers. It's meant for people who want to find some music, they for example didn't listen to for a long time. They will see from wich period the songs are, which genre they fall into and how they have been rated. This is a different starting point than usual and will allow the user to probably find other music, than without the tool.
The people that will use our tool are familiar with the internet and use it quite often. Moreover they are using their computer a lot at home and have their pc or mac in the living room where it is not only used to work, but also as multimedia station. That means they play video, music and watch TV with their computer.
They are also interessted in music and they are buying the music from the internet and not buying the songs on a CD. If they buy a CD they rip the content on the pc to have it in their music library.
What are the special interests of our target group?
I think our target group is quite young, probably creative and I suppose they have some sort of interest in playing around with things a bit. As we are analysing iTunes the target group likes nice and neat design, cool tools, cool features and gimmicks. They should use the visualisation mainly for playing around with their music collection.
Our target group has more interesst in mp3s than they have in buying CDs and playing them from their CD player. They have all their music stored on their computer and want to have many possibilities to browse through their collection. They are looking for new ways of finding music so that they can listen to they music they like and the music they feel for in the moment. Instead of putting a CD into the HiFi set they want to see what they have in their collection that suits their mood.
Are there any known / often used Methods / Visualisation Techniques?
Up to now there are no real visualisation techniques in the area of music players like iTunes. The only visualisation techniques can be found at playback the playback time of a song, when the music itself is visualised.
For representing the Songs of a library ususally lists or tables are used. That means this is not a real graphic visualisation, it's just a representation of the data, that the user can read and use to make for example playlists. Especially in iTunes the possibilities of combining many ID3 attributes to get a playlist are vast. You can for example say you want only songs from 1980 to 1984, that are in the rock genre, that have a rating between 1 and 3 stars, and are not from Guns n' Roses. So you can do quite a lot of thins with it, but there is no visualisation behind it. You can choose all that, but you can not choose it graphically which can make this much easier, faster, or just more interessting.
Purpose of the Visualisation
What should be achieved with the Visualisation?
Which tasks should be solved?
Warum haben wir nur wenige Datensätze heraus genommen?
Questions that can be solved using this Visualisation
Designproposal
Which kinds of Visualisation should be used?
- Focus & Context: Tiled Multi-Level Browser (Slides 0, Page 16)
- Scatterplot + Color and Shape concept
- details on demand: live choosable sliders for attribute ranges
- details on demand window
- zooming
- Linking & Brushing: Detail window containing rating distribution of selected songs (Slides 0, Page 25)
- Visual Encoding: volume, color