For ultrascale time-varying multivariate scientific simulation, there are often simply more data than can ever be viewed by human users. This is a rather fundamental problem that cannot be addressed just by parallel systems and visualization tools of large scale, which are
under focused attention by the SciDAC community. In this talk we present our recent work on creating summarizing visualization of time-varying multivariate data. The summary visualizations are created from relative distribution patterns among many variables and time-steps. In addition,
we describe ways to produce such visualization in a concept driven manner as well as the necessary parallel query-driven visualization infrastructure for supporting such capabilities. We show our results using driving applications such as climate modeling, especially to discover model differences, and combustion simulations.

Jian Huang is an associate professor of computer science at the University of Tennessee, Knoxville. His research focuses on large data visualization, and parallel, distributed and remote visualization. He received his PhD degree in computer science in 2001 from the Ohio State University. Dr. Huang's research has been funded by National Science Foundation and Department of Energy. He is a winner of DOE Early Career PI Award (2004-2007) and currently a co-PI of DOE SciDAC Institute of Ultrascale Visualization (2006-2011).