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For Scientific Data Discovery: Why Can't the Archive be More Like the Web?

Thomas H. Hinke, John A. Rushing, Shalini Kansal, Sara J. Graves, Heggere S. Ranganath: For Scientific Data Discovery: Why Can't the Archive be More Like the Web? SSDBM 1997: 96-99
@inproceedings{DBLP:conf/ssdbm/HinkeRKGR97,
  author    = {Thomas H. Hinke and
               John A. Rushing and
               Shalini Kansal and
               Sara J. Graves and
               Heggere S. Ranganath},
  editor    = {Yannis E. Ioannidis and
               David M. Hansen},
  title     = {For Scientific Data Discovery: Why Can't the Archive be More
               Like the Web?},
  booktitle = {Ninth International Conference on Scientific and Statistical
               Database Management, Proceedings, August 11-13, 1997, Olympia,
               Washington, USA},
  publisher = {IEEE Computer Society},
  year      = {1997},
  isbn      = {0-8186-7952-2},
  pages     = {96-99},
  ee        = {db/conf/ssdbm/HinkeRKGR97.html},
  crossref  = {DBLP:conf/ssdbm/97},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

This paper addresses the problem of acquiring from scientific data metadata that is descriptive of the actual content of the data. Scientists can use this content-based metadata in subsequent archive searches to find data sets of interest. Such metadata would be especially useful in large scientific archives such as NASA's Earth Observing System Data and Information System (EOSDIS). This paper presents two generic approaches for content-based metadata acquisition: target-dependent and target-independent. Both of these approaches are oriented toward characterizing datasets in terms of the scientific phenomena, such as mesoscale convective systems (severe storms) that they contain. In the target-dependent approach, the archived data is mined for particular phenomena of interest and polygons representing the phenomena are stored in a spatial database where they can be used in the data search process. In the target-independent approach, data is initially mined for deviations from normal and for trends. This data can then be used for subsequent searches for particular transient phenomena using the deviation data, or for phenomena related to trends. The paper describes results from implementing both of these approaches.

Copyright © 1997 by The Institute of Electrical and Electronic Engineers, Inc. (IEEE). Abstract used with permission.

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Yannis E. Ioannidis, David M. Hansen (Eds.): Ninth International Conference on Scientific and Statistical Database Management, Proceedings, August 11-13, 1997, Olympia, Washington, USA. IEEE Computer Society 1997, ISBN 0-8186-7952-2
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References

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Thomas H. Hinke, John A. Rushing, Heggere S. Ranganath, Sara J. Graves: Target-Independent Mining for Scientific Data: Capturing Transients and Trends for Phenomena Mining. KDD 1997: 187-190 BibTeX
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Abraham Silberschatz, Alexander Tuzhilin: On Subjective Measures of Interestingness in Knowledge Discovery. KDD 1995: 275-281 BibTeX
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