Literature DB >> 17356216

Value and relation display: interactive visual exploration of large data sets with hundreds of dimensions.

Jing Yang1, Daniel Hubball, Matthew O Ward, Elke A Rundensteiner, William Ribarsky.   

Abstract

Few existing visualization systems can handle large data sets with hundreds of dimensions, since high-dimensional data sets cause clutter on the display and large response time in interactive exploration. In this paper, we present a significantly improved multidimensional visualization approach named Value and Relation (VaR) display that allows users to effectively and efficiently explore large data sets with several hundred dimensions. In the VaR display, data values and dimension relationships are explicitly visualized in the same display by using dimension glyphs to explicitly represent values in dimensions and glyph layout to explicitly convey dimension relationships. In particular, pixel-oriented techniques and density-based scatterplots are used to create dimension glyphs to convey values. Multidimensional scaling, Jigsaw map hierarchy visualization techniques, and an animation metaphor named Rainfall are used to convey relationships among dimensions. A rich set of interaction tools has been provided to allow users to interactively detect patterns of interest in the VaR display. A prototype of the VaR display has been fully implemented. The case studies presented in this paper show how the prototype supports interactive exploration of data sets of several hundred dimensions. A user study evaluating the prototype is also reported in this paper.

Year:  2007        PMID: 17356216     DOI: 10.1109/TVCG.2007.1010

Source DB:  PubMed          Journal:  IEEE Trans Vis Comput Graph        ISSN: 1077-2626            Impact factor:   4.579


  2 in total

1.  Visual Pattern-Driven Exploration of Big Data.

Authors:  Michael Behrisch; Tobias Schreck; Robert Krüger; Nils Gehlenborg; Fritz Lekschas; Hanspeter Pfister
Journal:  2018 Int Symp Big Data Vis Immers Analyt (BDVA) (2018)       Date:  2018-11-15

2.  Probabilistic retrieval and visualization of biologically relevant microarray experiments.

Authors:  José Caldas; Nils Gehlenborg; Ali Faisal; Alvis Brazma; Samuel Kaski
Journal:  Bioinformatics       Date:  2009-06-15       Impact factor: 6.937

  2 in total

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