arXiv · astro-ph/0411589
Grist: Grid-based Data Mining for Astronomy
Abstract
The Grist project (http://grist.caltech.edu/) 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 workflow 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.
Explore related subjects
Keep this discovery
Explore connections, maps & timelines
Joseph C. Jacob, Roy Williams, Jogesh Babu, S. George Djorgovski, Matthew J. Graham, Daniel S. Katz, Ashish Mahabal, Craig D. Miller, Robert Nichol, Daniel E. Vanden Berk, Harshpreet Walia. 2004-11-19. Grist: Grid-based Data Mining for Astronomy. https://arxiv.org/abs/astro-ph/0411589
Cite the original work for its findings. Save a collection to share your selection of sources.