Fast Spatio-Temporal Data Mining of Large Geophysical Datasets

Paul Stolorz, H. Nakamura, E. Mesrobian, R. R. Muntz, E. C. Shek, J. R. Santos, J. Yi, K. Ng, S.-Y. Chien, C. R. Mechoso

The important scientific challenge of understanding global climate change is one that clearly requires the application of knowledge discovery and datamining techniques on a massive scale. Advances in parallel supercomputing technology, enabling high-resolution modeling, as well as in sensor technology, allowing data capture on an unprecedented scale, conspire to overwhelm present-day analysis approaches. We present here early experiences with a prototype exploratory data analysis environment, CON-QUEST, designed to provide content-based access to such massive scientific datasets. CON-QUEST (CONtent-based Querying in Space and Time) employs a combination of workstations and massively parallel processors (MPPs) to mine geophysical datasets possessing a prominent temporal component. It is designed to enable complex multi-modal interactive querying and knowledge discovery, while simultaneously coping with the extraordinary computational demands posed by the scope of the datasets involved. After outlining a working prototype, we concentrate here on the description of several associated feature extraction algorithms implemented on MPP platforms, together with some typical results.

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