Interactive Selection of Multivariate Features in Large Spatiotemporal Data

Jian Huang
Seminar

CT

Argonne National Laboratory
Building 240/Room 4301
Host Tom Peterka
Event Sponsor Mathematics and Computing Science

Selecting meaningful features is central in the analysis of scientific data. Today's multivariate scientific datasets are often large and complex making it difficult to define general features of interest significant to scientific applications. To address this problem, we propose three general, spatiotemporal metrics to quantify the significant properties of data features--concentration, continuity and co-occurrence, named collectively as CO3. We implemented an interactive visualization system to investigate complex multivariate time-varying data from satellite remote sensing with great spatial resolutions, as well as from real-time continental-scale power grid monitoring with great temporal resolutions. The system integrates CO3 metrics with an elegant multi-space user interaction tool to provide various forms of quantitative user feedback. Through these, the system supports an iterative user-driven analysis process. Our findings demonstrate that the CO3 metrics are useful for simplifying the problem space and revealing potential unknown possibilities of scientific discoveries by assisting users to effectively select significant features and groups of features for visualization and analysis. Users can then comprehend the problem better and design future studies using newly discovered scientific hypotheses.

Bio:
Jian Huang is an associate professor in the Department of Electrical Engineering and Computer Science and also associate director of the NSF funded remote data analysis and visualization center at the University of Tennessee, Knoxville. His research focuses on large data visualization, multivariate visualization, and parallel, distributed and remote visualization. He received a BEng in electrical engineering from the Nanjing University of Posts and Telecom, China in 1996. During his subsequent graduate study at the Ohio State University, he was awarded an MS degree in computer science and an MS degree in biomedical engineering, both in 1998, and a PhD degree in computer science in 2001. Dr. Huang's research has been funded by National Science Foundation and Department of Energy. Dr. Huang is a recipient of DOE Early Career Principal Investigator Award in 2004.