Interactive Visual Analysis of Set-Typed Data

While it is quite typical to deal with attributes of different data types in the visualization of heterogeneous and multivariate datasets, most existing techniques still focus on the most usual data types such as numerical attributes or strings. In this paper we present a new approach to the interac...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2008-11, Vol.14 (6), p.1340-1347
Main Authors: Freiler, W., Matkovic, K., Hauser, H.
Format: Article
Language:English
Subjects:
Algorithms
Categorical Data Visualization
Cleaning
Computer Graphics
Computer Simulation
Computer Society
Data analysis
Data mining
Data visualization
Database Management Systems
Databases, Factual
Dealing
Education
Exploration
Focus+Context Visualization
Focusing
Histograms
Index Terms&#8212
Information analysis
Information Storage and Retrieval - methods
Interactive
Interactive Visual Analysis
Interactive Visualization
Models, Theoretical
Multidimensional Multivariate Data Visualization
Multidimensional systems
Multiple Coordinated Views
Strings
Studies
Tables (data)
Tensile stress
User-Computer Interface
Videos
Visual
Visualization
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Summary:While it is quite typical to deal with attributes of different data types in the visualization of heterogeneous and multivariate datasets, most existing techniques still focus on the most usual data types such as numerical attributes or strings. In this paper we present a new approach to the interactive visual exploration and analysis of data that contains attributes which are of set type. A set-typed attribute of a data item - like one cell in a table - has a list of nGt=0 elements as its value. We present the setpsilaopsilagram as a new visualization approach to represent data of set type and to enable interactive visual exploration and analysis. We also demonstrate how this approach is capable to help in dealing with datasets that have a larger number of dimensions (more than a dozen or more), especially also in the context of categorical data. To illustrate the effectiveness of our approach, we present the interactive visual analysis of a CRM dataset with data from a questionnaire on the education and shopping habits of about 90000 people.
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2008.144