F. M. Hoffman, J. W. Larson, R. T. Mills, B. J. Brooks, A. R. Ganguly, W. W. Hargrove, J. Huang, J. Kumar, R. R. Vatsavai, "Data Mining in Earth System Science (DMESS 2011)," Procedia Computer Science, vol. 4, 2012, pp. 1450-1455. Also Preprint ANL/MCS-P2046-0212, February 2012. [pdf]
From field scale measurements to global climate simulations and remote sensing, the growing body of very large and long time series Earth science data are increasingly difficult to analyze, visualize, and interpret. Data mining, information theoretic, and machine learning techniques such as cluster analysis, singular value decomposition, block entropy, Fourier and wavelet analysis, phase space reconstruction, and artificial neural networks are being applied to problems of segmentation, feature extraction, change detection, model data comparison, and model validation. The size and complexity of Earth science data exceed the limits of most analysis tools and the capacities of desktop computers. New scalable analysis and visualization tools, running on parallel cluster computers and supercomputers, are required to analyze data of this magnitude. This workshop will demonstrate how data mining techniques are applied in the Earth sciences and describe innovative computer science methods that support analysis and discovery in the Earth sciences.