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Issue No.03 - March (2011 vol.17)

pp: 264-275

Luke J. Gosink , Pacific Northwest National Laboratory, Battelle Memorial Institute, Richland

Christoph Garth , University of California, Davis, Davis

John C. Anderson , University of California, Davis, Davis

E. Wes Bethel , Lawrence Berkeley National Laboratory, Berkeley

Kenneth I. Joy , University of California, Davis, Davis

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2010.80

ABSTRACT

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 data sets. This paper extends the utility of QDV strategies with a statistics-based framework that integrates nonparametric 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 maybe used to facilitate the refinement of constraints over variables expressed in a user's query. We apply our method to data sets from two different scientific domains to demonstrate its broad applicability.

INDEX TERMS

Query-driven visualization, multivariate analysis, kernel density estimation.

CITATION

Luke J. Gosink, Christoph Garth, John C. Anderson, E. Wes Bethel, Kenneth I. Joy, "An Application of Multivariate Statistical Analysis for Query-Driven Visualization",

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