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

Revision as of 20:28, 12 November 2005

Topic

On-line Music Portals: Analyzing the Users' Activities

Area of Application

This section describes the application area of MICE - Music Investigation and Clustering Environment.

[Image:MICELogo.gif]

Analysis of Application Area

General Description

Shopping analysis, Cluster / Pattern analysis, Web based area of application, all data analysed comes form online users, areas of application include shopping optimazation, web application development, technical optimization. The tool might also be interesting for pure online stores.

Special Issues

We identified the following spots of interest:

The user of MICE has different intentions than the person the data is about
Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge
The questions wanted to be solved will gain on complexity once they are answered (report chain)
The answering will not be a single "result" but a (iterative) process.
Heavy task to realize a graphical abstryact view on the data.
...

Analysis of the Dataset

Description of the Datatypes

Description of the Datastructures

Target Group

Identifying the Target

The target for the exploration tool are mainly shopping analysts and web developer. It is the aim to suit the needs of persone who run the on-line portal (since they are interested in optimization). On the other hand one must not forget about the technical aspects, abd by that help the developers of such systems. After all this groups should pass on their

Special Issues of the Target Group

Known Solutions / Methods (related to the traget group)

Intended Purpose

Goals and Objectives

Problems and Tasks to Solve

Example Questions

Proposed Design

Types of Visualization Applied

Visual Mapping

(Datadimension => Attribute)

@@ Line 4: / Line 4: @@
 ==Area of Application==
 This section describes the application area of
-'''MISE - Music Investigation and Clustering Environment'''.
+'''MICE - Music Investigation and Clustering Environment'''.
+[Image:MICELogo.gif]
 ===Analysis of Application Area===
-Web based, delivery of information, additionally selling related products. Mainstay of rewards: advertisement. Main information delivered: artists, music, genre, what is new, what are the ''tops'', what are the ''flops''.
 ====General Description====
-The source of the data investigated are on-line music portals such as .... Those type of portals provide information for music lovers about their favorite music and genre. Moreover it provides the possibility to shop music and assets of products related to the music. Another mainstay of the rewards is advertisement. The success heavily depends on getting the consumer an easy entry and easy access to his or her preffered music. As a matter of fact this is not a-priori known, so the portal itsself has to provide good techniques to guied the visitor.
+Shopping analysis, Cluster / Pattern analysis, Web based area of application, all data analysed comes form online users, areas of application include shopping optimazation, web application development, technical optimization. The tool might also be interesting for pure online stores.
-This gives plenty of source for invesigating the data from an existing portal to find out what may help future development and answer interesting questions of optimazation.
 ====Special Issues====
 We identified the following spots of interest:
-# The interaction of the user is heavily related to the exisiting portal the user is visiting (from that point of view the data is very much affected)
+# The user of MICE has different intentions than the person the data is about
-# The users do have very diversified usage, hence they do not all necessarily visit the page on a set purpose (maybe this develops during the visit, woudl be nice to find out on which occasion)
+# Basket Case Analysis and Cluster Analysis are heavily statistical techniques and quite intense in terms of required knowledge
+# The questions wanted to be solved will gain on complexity once they are answered (report chain)
+# The answering will not be a single "result" but a (iterative) process.
+# Heavy task to realize a graphical abstryact view on the data.
 # ...