An application of multivariate statistical analysis for Query-Driven Visualization

© 2011 IEEE

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on visualization and computer graphics. - 1996. - 17(2011), 3 vom: 25. März, Seite 264-75
1. Verfasser: Gosink, Luke J (VerfasserIn)
Weitere Verfasser: Garth, Christoph, Anderson, John C, Bethel, E Wes, Joy, Kenneth I
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2011
Zugriff auf das übergeordnete Werk:IEEE transactions on visualization and computer graphics
Schlagworte:Journal Article Research Support, U.S. Gov't, Non-P.H.S.
LEADER 01000naa a22002652 4500
001 NLM198356552
003 DE-627
005 20231223212538.0
007 cr uuu---uuuuu
008 231223s2011 xx |||||o 00| ||eng c
024 7 |a 10.1109/TVCG.2010.80  |2 doi 
028 5 2 |a pubmed24n0661.xml 
035 |a (DE-627)NLM198356552 
035 |a (NLM)20498506 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Gosink, Luke J  |e verfasserin  |4 aut 
245 1 3 |a An application of multivariate statistical analysis for Query-Driven Visualization 
264 1 |c 2011 
336 |a Text  |b txt  |2 rdacontent 
337 |a ƒaComputermedien  |b c  |2 rdamedia 
338 |a ƒa Online-Ressource  |b cr  |2 rdacarrier 
500 |a Date Completed 20.04.2012 
500 |a Date Revised 24.04.2012 
500 |a published: Print 
500 |a Citation Status MEDLINE 
520 |a © 2011 IEEE 
520 |a 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 
650 4 |a Journal Article 
650 4 |a Research Support, U.S. Gov't, Non-P.H.S. 
700 1 |a Garth, Christoph  |e verfasserin  |4 aut 
700 1 |a Anderson, John C  |e verfasserin  |4 aut 
700 1 |a Bethel, E Wes  |e verfasserin  |4 aut 
700 1 |a Joy, Kenneth I  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t IEEE transactions on visualization and computer graphics  |d 1996  |g 17(2011), 3 vom: 25. März, Seite 264-75  |w (DE-627)NLM098269445  |x 1941-0506  |7 nnns 
773 1 8 |g volume:17  |g year:2011  |g number:3  |g day:25  |g month:03  |g pages:264-75 
856 4 0 |u http://dx.doi.org/10.1109/TVCG.2010.80  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d 17  |j 2011  |e 3  |b 25  |c 03  |h 264-75