Analyzing information transfer in time-varying multivariate data

Chaoli Wang, Hongfeng Yu, Ray W. Grout, Kwan-Liu Ma, Jacqueline H. Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Scopus citations


Effective analysis and visualization of time-varying multivariate data is crucial for understanding complex and dynamic variable interaction and temporal evolution. Advances made in this area are mainly on query-driven visualization and correlation exploration. Solutions and techniques that investigate the important aspect of causal relationships among variables have not been sought. In this paper, we present a new approach to analyzing and visualizing time-varying multivariate volumetric and particle data sets through the study of information flow using the information-theoretic concept of transfer entropy. We employ time plot and circular graph to show information transfer for an overview of relations among all pairs of variables. To intuitively illustrate the influence relation between a pair of variables in the visualization, we modulate the color saturation and opacity for volumetric data sets and present three different visual representations, namely, ellipse, smoke, and metaball, for particle data sets. We demonstrate this information-theoretic approach and present our findings with three time-varying multivariate data sets produced from scientific simulations.

Original languageEnglish (US)
Title of host publicationIEEE Pacific Visualization Symposium 2011, PacificVis 2011 - Proceedings
Number of pages8
StatePublished - May 11 2011
Event4th IEEE Pacific Visualization Symposium 2011, PacificVis 2011 - Hong Kong, China
Duration: Mar 1 2011Mar 4 2011


Other4th IEEE Pacific Visualization Symposium 2011, PacificVis 2011
CityHong Kong

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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