Scalable pattern recognition for large-scale scientific data mining

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Description

Our ability to generate data far outstrips our ability to explore and understand it. The true value of this data lies not in its final size or complexity, but rather in our ability to exploit the data to achieve scientific goals. The data generated by programs such as ASCI have such a large scale that it is impractical to manually analyze, explore, and understand it. As a result, useful information is overlooked, and the potential benefits of increased computational and data gathering capabilities are only partially realized. The difficulties that will be faced by ASCI applications in the near future ... continued below

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14 p.

Creation Information

Kamath, C. & Musick, R. March 23, 1998.

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Description

Our ability to generate data far outstrips our ability to explore and understand it. The true value of this data lies not in its final size or complexity, but rather in our ability to exploit the data to achieve scientific goals. The data generated by programs such as ASCI have such a large scale that it is impractical to manually analyze, explore, and understand it. As a result, useful information is overlooked, and the potential benefits of increased computational and data gathering capabilities are only partially realized. The difficulties that will be faced by ASCI applications in the near future are foreshadowed by the challenges currently facing astrophysicists in making full use of the data they have collected over the years. For example, among other difficulties, astrophysicists have expressed concern that the sheer size of their data restricts them to looking at very small, narrow portions at any one time. This narrow focus has resulted in the loss of ``serendipitous`` discoveries which have been so vital to progress in the area in the past. To solve this problem, a new generation of computational tools and techniques is needed to help automate the exploration and management of large scientific data. This whitepaper proposes applying and extending ideas from the area of data mining, in particular pattern recognition, to improve the way in which scientists interact with large, multi-dimensional, time-varying data.

Physical Description

14 p.

Notes

OSTI as DE98058345

Other: FDE: PDF; PL:

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  • Other Information: PBD: 23 Mar 1998

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  • Other: DE98058345
  • Report No.: UCRL-ID--130245
  • Grant Number: W-7405-ENG-48
  • DOI: 10.2172/310913 | External Link
  • Office of Scientific & Technical Information Report Number: 310913
  • Archival Resource Key: ark:/67531/metadc687411

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

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  • March 23, 1998

Added to The UNT Digital Library

  • July 25, 2015, 2:20 a.m.

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  • April 10, 2017, 1:40 p.m.

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Kamath, C. & Musick, R. Scalable pattern recognition for large-scale scientific data mining, report, March 23, 1998; California. (digital.library.unt.edu/ark:/67531/metadc687411/: accessed December 13, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.