An Application of Multivariate Statistical Analysis for Query-Driven Visualization

Journal Article · Tue Mar 01 04:00:00 EST 2011 · IEEE Transactions on Visualization and Computer Graphics

DOI:https://doi.org/10.1109/TVCG.2010.80· OSTI ID:989843

Gosink, Luke J. ^[1]; Garth, Christoph ^[2]; Anderson, John C. ^[2]; Bethel, E. Wes ^[3]; Joy, Kenneth I. ^[2]

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1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
2. Univ. of California, Davis, CA (United States)
3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Driven by the ability to generate ever-larger, increasingly complex data, there is an urgent need in the scientific community for scalable analysis methods that can rapidly identify salient trends in scientific data. Query-Driven Visualization (QDV) strategies are among the small subset of techniques that can address both large and highly complex datasets. This paper extends the utility of QDV strategies with a statistics-based framework that integrates non-parametric distribution estimation techniques with a new segmentation strategy to visually identify statistically significant trends and features within the solution space of a query. In this framework, query distribution estimates help users to interactively explore their query's solution and visually identify the regions where the combined behavior of constrained variables is most important, statistically, to their inquiry. Our new segmentation strategy extends the distribution estimation analysis by visually conveying the individual importance of each variable to these regions of high statistical significance. We demonstrate the analysis benefits these two strategies provide and show how they may be used to facilitate the refinement of constraints over variables expressed in a user's query. We apply our method to datasets from two different scientific domains to demonstrate its broad applicability.

• Research Organization: Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, CA (US)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
• DOE Contract Number: AC02-05CH11231
• OSTI ID: 989843
• Report Number(s): LBNL--3536E
• Journal Information: IEEE Transactions on Visualization and Computer Graphics, Journal Name: IEEE Transactions on Visualization and Computer Graphics Journal Issue: 3 Vol. 17; ISSN 1077-2626
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
COMPUTER GRAPHICS
DISTRIBUTION
LAWRENCE BERKELEY LABORATORY
query-driven visualization
multivariate analysis
kernel density estimation