Data modeling for virtual observatory data mining

Holger M. Jaenisch, James Handley, Albert C. Lim, Miroslav Filipovic, Graeme L. White, Alex Hons, Gary Deragopian, Mark Schneider, Matthew Edwards, Peter J. Quinn, Alan Bridger

    Research output: Chapter in Book / Conference PaperConference Paper

    Abstract

    ![CDATA[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.]]
    Original languageEnglish
    Title of host publicationOptimizing Scientific Return for Astronomy through Information Technologies: Proceedings of the SPIE Conference, held 21-25 June, 2004 in Glasgow, Scotland, U.K.
    PublisherSPIE--The International Society for Optical Engineering
    Number of pages23
    ISBN (Print)0819454257
    Publication statusPublished - 2004
    EventModeling and Systems Engineering for Astronomy -
    Duration: 1 Jan 2004 → …

    Conference

    ConferenceModeling and Systems Engineering for Astronomy
    Period1/01/04 → …

    Keywords

    • National Virtual Observatory (U.S.)
    • data mining
    • data modeling
    • eclipsing binaries
    • extrasolar planets
    • optics, adaptive

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