ExtractVis: dynamic visualization of extracting multidimensional data

Authors:
Zhao Xiao;Changbo Wang;Yuhua Liu;Chenming Pang;Peng Ye
Affiliations:
East China Normal University, Shanghai, P. R. China;East China Normal University, Shanghai, P. R. China;East China Normal University, Shanghai, P. R. China;East China Normal University, Shanghai, P. R. China;Soochow University Suzhou, P. R. China
Venue:
Proceedings of the 5th International Symposium on Visual Information Communication and Interaction
Year:
2012

Citing 6
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Abstract

Due to the excessive items and multiple dimensions of parallel data, traditional visualization methods can not show the prominent information from their characters. This paper proposes a novel method of entity extracting to perform the multi-scale and hierarchical visualization of multi-attribute data set. Firstly, the relationship between these characters can be expressed as entity-relationship and data dimension is expressed as entity attributes, which can eliminate data redundancy and reduce data dimensions. Then a scalable dynamic visualization mode is proposed to show the characters at different levels of details. The method can interactively operate to visualize different data sets, such as electronic commerce data, weather forecast data, and gene expressions data, generating effective visualization results.