An application of multivariate statistical analysis for Query-Driven Visualization.
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.
Gosink
Luke J
LJ
Pacific Northwest National Laboratory, Mail Stop K7-20, Battelle Memorial Institute, PO Box 999, Richland, Washington 99352, USA. luke.gosink@pnl.gov
Garth
Christoph
C
Anderson
John C
JC
Bethel
E Wes
EW
Joy
Kenneth I
KI
D003196:Computer Graphics; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D016247:Information Storage and Retrieval / Q000379:methods*; D015999:Multivariate Analysis; D014584:User-Computer Interface