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SkyQuery: Federating Astronomy Archives
Found in: Computing in Science & Engineering
By Tamas Budavari,Laszlo Dobos,Alexander S. Szalay
Issue Date:May 2013
pp. 12-20
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. SkyQu...
 
Incremental and Parallel Analytics on Astrophysical Data Streams
Found in: 2012 SC Companion: High Performance Computing, Networking, Storage and Analysis (SCC)
By Dmitry Mishin,Tamas Budavari,Alexander Szalay,Yanif Ahmad
Issue Date:November 2012
pp. 1078-1086
Stream processing methods and online algorithms are increasingly appealing in the scientific and large-scale data management communities due to increasing ingestion rates of scientific instruments, the ability to produce and inspect results interactively, ...
 
Grid-Based Galaxy Morphology Analysis for the National Virtual Observatory
Found in: SC Conference
By Ewa Deelman, Raymond Plante, Carl Kesselman, Gurmeet Singh, Mei-Hui Su, Gretchen Greene, Robert Hanisch, Niall Gaffney, Antonio Volpicelli, James Annis, Vijay Sekhri, Tamas Budavari, Maria Nieto-Santisteban, William O'Mullane, David Bohlender, Tom McGlynn,
Issue Date:November 2003
pp. 47
As part of the development of the National Virtual Observatory (NVO), a Data Grid for astronomy, we have developed a prototype science application to explore the dynamical history of galaxy clusters by analyzing the galaxies' morphologies. The purpose of t...
 
JAWS: Job-Aware Workload Scheduling for the Exploration of Turbulence Simulations
Found in: SC Conference
By Xiaodan Wang, Eric Perlman, Randal Burns, Tanu Malik, Tamas Budavári, Charles Meneveau, Alexander Szalay
Issue Date:November 2010
pp. 1-11
We present JAWS, a job-aware, data-driven batch scheduler that improves query throughput for data-intensive scientific database clusters. As datasets reach petabyte-scale, workloads that scan through vast amounts of data to extract features are gaining imp...
 
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