Mining Temporally-Varying Phenomena in Scientific Datasets
Machiraju, R., Parthasarathy, S., Wilkins, J., Thompson, D., Gatlin, B., Richie, D., Choy, T., Jiang, M., Mehta, S., Coatney, M., Barr, S., & Hazzard, K. (2004). Mining Temporally-Varying Phenomena in Scientific Datasets. In H. Kargupta, A. Joshi, K. Sivakumar, and Y. Yesha (Eds.), Data Mining: Next Generation Challenges and Future Directions. Cambridge, MA: AAAI Press/The MIT Press. 273-290.
Simulation is enhancing and, in many instances, replacing experimentation as a means to gain insight into complex physical phenomena. Recent advances in computer hardware and numerical methods have made it possible to simulate physical phenomena at very fine temporal and spatial resolutions. Unfortunately, given the enormous sizes of the datasets involved, analyzing datasets produced by these simulations is extremely challenging. In order to more fully exploit simulation, the analysis of these large datasets must advance beyond current techniques that are based on interactive visualization. We outline our vision for one such approach and describe progress on a unified framework that promises to provide a novel method to explore large simulation datasets. We illustrate its application to two disparate science drivers – temporally varying solid and fluid systems. In both applications, there are hidden hierarchies of features as well as many abstract multidimensional feature characterizations (e.g. shapes). Through this framework, we offer a systematic approach to detect, characterize, and track meta-stable features as well as formulate hypotheses about their evolution – an important step in extracting vital information from such complex systems.