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Proceedings Paper

Data modeling for virtual observatory data mining
Author(s): Holger M. Jaenisch; James Handley; Albert Lim; Miroslav Filipovic; Graeme White; Alex Hons; Gary Deragopian; Mark Schneider; Matthew Edwards
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Paper Abstract

< 869.47 -3.27 41.37 602.25 10053.48 620.0042> We propose a novel approach for index-tagging Virtual Observatory data files with descriptive statistics enabling rapid data mining and mathematical modeling. This is achieved by calculating at data collection time 6 standard moments as descriptive file tags. Data Change Detection Models are derived from these tags and used to filter databases for similar or dissimilar information such as stellar spectra, photometric data, images, and text. Currently, no consistent or reliable method for searching, collating, and comparing 2-D imagery exists. Traditionally, methods used to address these data problems are disparate and unrelated to text data mining and extraction. We explore the use of mathematical Data Models as a unifying tool set for enabling data mining across all data class domains.

Paper Details

Date Published: 16 September 2004
PDF: 23 pages
Proc. SPIE 5493, Optimizing Scientific Return for Astronomy through Information Technologies, (16 September 2004); doi: 10.1117/12.548739
Show Author Affiliations
Holger M. Jaenisch, James Cook Univ. (Australia)
Alabama A&M Univ. (United States)
James Handley, James Cook Univ. (Australia)
Albert Lim, James Cook Univ. (Australia)
Miroslav Filipovic, Univ. of Western Sydney (Australia)
James Cook Univ. (Australia)
Graeme White, James Cook Univ. (Australia)
Alex Hons, James Cook Univ. (Australia)
Gary Deragopian, James Cook Univ. (Australia)
Mark Schneider, James Cook Univ. (Australia)
Matthew Edwards, Alabama A&M Univ. (United States)

Published in SPIE Proceedings Vol. 5493:
Optimizing Scientific Return for Astronomy through Information Technologies
Peter J. Quinn; Alan Bridger, Editor(s)

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