Data mining in earth system science (DMESS 2011)

Forrest M. Hoffman, J. Walter Larson, Richard Tran Mills, Bjørn Gustaf J. Brooks, Auroop R. Ganguly, William W. Hargrove, Jian Huang, Jitendra Kumar, Ranga R. Vatsavai

Research output: Contribution to journalConference articlepeer-review

16 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)1450-1455
Number of pages6
JournalProcedia Computer Science
Volume4
DOIs
StatePublished - 2011
Event11th International Conference on Computational Science, ICCS 2011 - Singapore, Singapore
Duration: Jun 1 2011Jun 3 2011

Funding

The DMESS 2011 co-conveners—FMH, JWL, and RTM—wish to thank the Workshop Program Committee for their assistance in reviewing submitted papers. The Program Committee consisted of Michael W. Berry, Bjørn-Gustaf J. Brooks, Rebecca A. Efroymson, Sara J. Graves, William W. Hargrove, Jian Huang, Robert L. Jacob, Jitendra Ku-mar, Vipin Kumar, and Ranga R. Vatsavai. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. Argonne National Laboratory is managed by UChicago Argonne, LLC, for the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. The submitted manuscript has been authored by a contractor of the U.S. Government; accordingly, the U.S. Government retains a non-exclusive, royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so, for U.S. Government purposes.

Keywords

  • Change detection
  • Data mining
  • High performance computing
  • Remote sensing
  • Segmentation
  • Synthesis
  • Visualization

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