Grist: Grid-based Data Mining for Astronomy

Joseph C. Jacob, Daniel S. Katz, Craig D. Miller, Harshpreet Walia, Roy Williams, S. George Djorgovski, Matthew Graham, Ashish Mahabal, Jogesh Babu, Daniel E. Vanden Berk and Robert Nichol (2005) Grist: Grid-based Data Mining for Astronomy. In: Astronomical Data Analysis Software and Systems XIV. Astronomical Society of the Pacific Conference Series (XXX). Astronomical Society of the Pacific, San Francisco, CA, P1.3.8.

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Abstract

The Grist project is developing a grid-technology based system as a research environment for astronomy with massive and complex datasets. This knowledge extraction system will consist of a library of distributed grid services controlled by a work ow system, compliant with standards emerging from the grid computing, web services, and virtual observatory communities. This new technology is being used to find high redshift quasars, study peculiar variable objects, search for transients in real time, and fit SDSS QSO spectra to measure black hole masses. Grist services are also a component of the “hyperatlas” project to serve high-resolution multi-wavelength imagery over the Internet. In support of these science and outreach objectives, the Grist framework will provide the enabling fabric to tie together distributed grid services in the areas of data access, federation, mining, subsetting, source extraction, image mosaicking, statistics, and visualization.

EPrint Type:	Book Section
Additional Information:	Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, and was sponsored by the National Science Foundation through an agreement with the National Aeronautics and Space Administration. Also available: arXiv:astro-ph/0411589 v1 19 Nov 2004
Subjects:	All Records
ID Code:	126
Deposited By:	Sarah M. Emery
Deposited On:	15 February 2006
Record Number:	CaltechCACR:2005.118
Official Persistent URL:	http://resolver.caltech.edu/CaltechCACR:2005.118
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