DMCA
1 An Application of Multivariate Statistical Analysis
BibTeX
@MISC{Visualization_1an,
author = {For Query-driven Visualization and Luke J. Gosink and Christoph Garth and John C. Anderson and E. Wes Bethel and Kenneth I. Joy},
title = {1 An Application of Multivariate Statistical Analysis},
year = {}
}
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Abstract
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 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.
Keyphrases
multivariate statistical analysis new segmentation strategy scalable analysis method complex datasets distribution estimation analysis qdv strategy high statistical significance query-driven visualization analysis benefit urgent need query solution combined behavior query distribution estimate complex data constrained variable scientific data broad applicability solution space individual importance user query small subset significant trend non-parametric distribution estimation technique statistics-based framework scientific community abstract driven salient trend different scientific domain
