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Issue No.03 - May-June (2013 vol.15)
pp: 12-20
Tamas Budavari , The Johns Hopkins University
Laszlo Dobos , Eötvös Loránd University
Alexander S. Szalay , The Johns Hopkins University
ABSTRACT
Astronomical discoveries often happen at the edge of our observational capabilities. To fully analyze telescopic images, researchers must combine data from separate telescopes, but large volumes of data with intrinsic differences make this difficult. SkyQuery, a scalable query engine, helps with this process.
INDEX TERMS
Image processing, Databases, Astronomy, Telescopes, Photonics, Distributed databases, Scientific computing, Query processing, Virtual environments, Probability, scientific computing, distributed database, query language, probabilistic cross-match, virtual observatory
CITATION
Tamas Budavari, Laszlo Dobos, Alexander S. Szalay, "SkyQuery: Federating Astronomy Archives", Computing in Science & Engineering, vol.15, no. 3, pp. 12-20, May-June 2013, doi:10.1109/MCSE.2013.41
REFERENCES
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2. N. Li and A.R. Thakar, “CasJobs and MyDB: A Batch Query Workbench,” Computing in Science & Eng., vol. 10, no. 1, pp. 18–29.
3. T. Budavari et al., “SkyQuery—A Prototype Distributed Query Web Service for the Virtual Observatory,” Proc. Conf. Astronomical Data Analysis Software and Systems, Astronomical Soc. of the Pacific, vol. 295, 2003, pp. 31–34.
4. T. Malik et al., “SkyQuery: A WebService Approach to Federate Databases,” Proc. First Biennial Conf. Innovative Data Systems Research, VLDB Foundation, 2003; www-db.cs.wisc.edu/cidr/cidr2003/program p17.pdf.
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6. J. Gray et al., The Zones Algorithm for Finding Points-Near-a-Point or Cross-Matching Spatial Datasets, tech. report 2006-52, Microsoft Research, Apr. 2006; arXiv:cs/070 1171.
7. T. Budavari and A.S. Szalay, “Probabilistic Cross-Identification of Astronomical Sources,” Astrophysical J., vol. 679; doi:10.1086/58 7156.
8. J. Gray et al., “Scientific Data Management in the Coming Decade,” ACM SIGMOD Record, vol. 34, no. 4, 2005, pp. 34–41.
9. T. Hey, S. Tansley, and K. Tolle, The Fourth Paradigm: Data-Intensive Scientific Discovery, Microsoft Research, 2009; http://research.microsoft.com/en-us/collaboration fourthparadigm.
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