An Application of Multivariate Statistical Analysis for Query-Driven Visualization

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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 ... continued below

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Gosink, Luke J.; Garth, Christoph; Anderson, John C.; Bethel, E. Wes & Joy, Kenneth I. March 1, 2010.

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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.

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  • Journal Name: IEEE Transactions on Visualization and Computer Graphics

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  • Report No.: LBNL-3536E
  • Grant Number: DE-AC02-05CH11231
  • Office of Scientific & Technical Information Report Number: 989843
  • Archival Resource Key: ark:/67531/metadc1012507

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

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  • March 1, 2010

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  • Oct. 14, 2017, 8:36 a.m.

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  • Oct. 17, 2017, 5:59 p.m.

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Gosink, Luke J.; Garth, Christoph; Anderson, John C.; Bethel, E. Wes & Joy, Kenneth I. An Application of Multivariate Statistical Analysis for Query-Driven Visualization, article, March 1, 2010; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc1012507/: accessed December 17, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.