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Hierarchical difference scatterplots interactive visual analysis of data cubes
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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration table of contents
Paris, France
Pages 56-65  
Year of Publication: 2009
ISBN:978-1-60558-670-0
Authors
Harald Piringer  VRVis Research Center, Vienna, Austria
Matthias Buchetics  VRVis Research Center, Vienna, Austria
Helwig Hauser  University of Bergen, Norway
Eduard Gröller  Vienna University of Technology, Austria
Sponsors
: PASCAL2 - Pattern Analysis, Statistical Modelling and Computational Learning
: Helsinki Institute for Information Technology HIIT
: VisMaster, a European FP7 Coordination Action Project focused on Visual Analytics
: Danube University Krems, Departement of Information and Knowledge Engineering (DUK)
: National Visualization and Analytics Center (NVAC)
Publisher
ACM  New York, NY, USA
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ABSTRACT

Data cubes as employed by On-Line Analytical Processing (OLAP) play a key role in many application domains. The analysis typically involves to compare categories of different hierarchy levels with respect to size and pivoted values. Most existing visualization methods for pivoted values, however, are limited to single hierarchy levels. The main contribution of this paper is an approach called Hierarchical Difference Scatterplot (HDS). A HDS allows for relating multiple hierarchy levels and explicitly visualizes differences between them in the context of the absolute position of pivoted values. We discuss concepts of tightly coupling HDS to other types of tree visualizations and propose the integration in a setup of multiple views, which are linked by interactive queries on the data. We evaluate our approaches by analyzing social survey data in collaboration with a domain expert.


REFERENCES

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Collaborative Colleagues:
Harald Piringer: colleagues
Matthias Buchetics: colleagues
Helwig Hauser: colleagues
Eduard Gröller: colleagues