Visualization Symposium, IEEE Pacific (2011)
Hong Kong, China
Mar. 1, 2011 to Mar. 4, 2011
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.
graph theory, data structures, data visualisation, entropy, particle data sets, information transfer analysis, time-varying multivariate data visualization, dynamic variable interaction, temporal evolution, query-driven visualization, correlation exploration, information flow, information-theoretic concept, transfer entropy, time plot, circular graph, color saturation, volumetric data sets, ellipse visual representation, smoke visual representation, metaball visual representation, Entropy, Data visualization, Histograms, Image color analysis, Combustion, Joints, Rendering (computer graphics)
"Analyzing information transfer in time-varying multivariate data," 2011 IEEE Pacific Visualization Symposium (PacificVis)(PACIFICVIS), Hong Kong, 2011, pp. 99-106.