IN43A-1721
Scalable Algorithms for Unsupervised Classification and Anomaly Detection in Large Geospatiotemporal Data Sets
Thursday, 17 December 2015
Poster Hall (Moscone South)
Richard T Mills1, Forrest M Hoffman2,3 and Jitendra Kumar3, (1)Intel Corporation, Hillsboro, OR, United States, (2)University of California Irvine, Department of Earth System Science, Irvine, CA, United States, (3)Oak Ridge National Laboratory, Climate Change Science Institute, Oak Ridge, TN, United States
Abstract:
The increasing availability of high-resolution geospatiotemporal datasets from sources such as observatory networks, remote sensing platforms, and computational Earth system models has opened new possibilities for knowledge discovery and mining of ecological data sets fused from disparate sources. Traditional algorithms and computing platforms are impractical for the analysis and synthesis of data sets of this size; however, new algorithmic approaches that can effectively utilize the complex memory hierarchies and the extremely high levels of available parallelism in state-of-the-art high-performance computing platforms can enable such analysis. We describe some unsupervised knowledge discovery and anomaly detection approaches based on highly scalable parallel algorithms for k-means clustering and singular value decomposition, consider a few practical applications thereof to the analysis of climatic and remotely-sensed vegetation phenology data sets, and speculate on some of the new applications that such scalable analysis methods may enable.
