paper_2011.bib

@inproceedings{Hoffman2011a,
  title = {{D}ata {M}ining in {E}arth {S}ystem {S}cience ({DMESS} 2011)},
  author = {Forrest M. Hoffman and J. Walter Larson and Richard Tran Mills and Bj{\o}rn-Gustaf J. Brooks and Auroop R. Ganguly and William W. Hargrove and Jian Huang and Jitendra Kumar and Ranga R. Vatsavai},
  booktitle = {Proceedings of the International Conference on Computational Science ({ICCS} 2011)},
  year = {2011},
  address = {Singapore},
  editor = {Mitsuhisa Sato and Satoshi Matsuoka and Peter M. Sloot and G. Dick {van Albada} and Jack Dongarra},
  month = {June},
  pages = {1450--1455},
  publisher = {Elsevier},
  series = {Procedia Computer Science},
  volume = {4},
  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.},
  dates = {1--3 June 2011},
  day = {1},
  doi = {10.1016/j.procs.2011.04.157},
  note = {\url{https://doi.org/10.1016/j.procs.2011.04.157}},
  file = {pubs/Hoffman_ICCS_2011.pdf},
  issn = {1877-0509},
  location = {Nanyang Technological University, Singapore},
  owner = {jkumar},
  timestamp = {2011.06.01}
}

@inproceedings{Kumar2011,
  title = {Parallel $k$-Means Clustering for Quantitative Ecoregion Delineation Using Large Data Sets},
  author = {Jitendra Kumar and Richard Tran Mills and Forrest M. Hoffman and William W. Hargrove},
  booktitle = {Proceedings of the International Conference on Computational Science ({ICCS} 2011)},
  year = {2011},
  address = {Singapore},
  editor = {Mitsuhisa Sato and Satoshi Matsuoka and Peter M. Sloot and G. Dick {van Albada} and Jack Dongarra},
  month = {June},
  pages = {1602--1611},
  publisher = {Elsevier},
  series = {Procedia Computer Science},
  volume = {4},
  abstract = {Identification of geographic ecoregions has long been of interest to environmental scientists and ecologists for identifying regions of similar ecological and environmental conditions. Such classifications are important for predicting suitable species ranges, for stratification of ecological samples, and to help prioritize habitat preservation and remediation efforts. Hargrove and Hoffman [1] and [2] have developed geographical spatio-temporal clustering algorithms and codes and have successfully applied them to a variety of environmental science domains, including ecological regionalization; environmental monitoring network design; analysis of satellite-, airborne-, and ground-based remote sensing, and climate model-model and model-measurement intercomparison. With the advances in state-of-the-art satellite remote sensing and climate models, observations and model outputs are available at increasingly high spatial and temporal resolutions. Long time series of these high resolution datasets are extremely large in size and growing. Analysis and knowledge extraction from these large datasets are not just algorithmic and ecological problems, but also pose a complex computational problem. This paper focuses on the development of a massively parallel multivariate geographical spatio-temporal clustering code for analysis of very large datasets using tens of thousands processors on one of the fastest supercomputers in the world.},
  dates = {1--3 June 2011},
  day = {1},
  doi = {10.1016/j.procs.2011.04.173},
  note = {\url{https://doi.org/10.1016/j.procs.2011.04.173}},
  file = {pubs/Kumar_ICCS_2011.pdf},
  issn = {1877-0509},
  location = {Nanyang Technological University, Singapore},
  owner = {jkumar},
  timestamp = {2011.06.02}
}

@inproceedings{Mills2011,
  title = {Cluster Analysis-Based Approaches for Geospatiotemporal Data Mining of Massive Data Sets for Identification of Forest Threats},
  author = {Richard Tran Mills and Forrest M. Hoffman and Jitendra Kumar and William W. Hargrove},
  booktitle = {Proceedings of the International Conference on Computational Science ({ICCS} 2011)},
  year = {2011},
  address = {Singapore},
  editor = {Mitsuhisa Sato and Satoshi Matsuoka and Peter M. Sloot and G. Dick {van Albada} and Jack Dongarra},
  month = {June},
  pages = {1612--1621},
  publisher = {Elsevier},
  series = {Procedia Computer Science},
  volume = {4},
  abstract = {We investigate methods for geospatiotemporal data mining of multi-year land surface phenology data (250 m$^2$ Normalized Difference Vegetation Index (NDVI) values derived from the Moderate Resolution Imaging Spectrometer (MODIS) in this study) for the conterminous United States (CONUS) as part of an early warning system for detecting threats to forest ecosystems. The approaches explored here are based on $k$-means cluster analysis of this massive data set, which provides a basis for defining the bounds of the expected or ``normal'' phenological patterns that indicate healthy vegetation at a given geographic location. We briefly describe the computational approaches we have used to make cluster analysis of such massive data sets feasible, describe approaches we have explored for distinguishing between normal and abnormal phenology, and present some examples in which we have applied these approaches to identify various forest disturbances in the CONUS.},
  dates = {1--3 June 2011},
  day = {1},
  doi = {10.1016/j.procs.2011.04.174},
  note = {\url{https://doi.org/10.1016/j.procs.2011.04.174}},
  file = {pubs/Mills_ICCS_2011.pdf},
  issn = {1877-0509},
  location = {Nanyang Technological University, Singapore},
  owner = {jkumar},
  timestamp = {2011.06.01}
}