Statistics – Applications
Scientific paper
Aug 2008
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008aspc..394..205n&link_type=abstract
Astronomical Data Analysis Software and Systems ASP Conference Series, Vol. 394, Proceedings of the conference held 23-26 Septem
Statistics
Applications
Scientific paper
The astronomy community has put tremendous efforts to provide global access to their distributed scientific datasets. Motivated by the expected data rates of several TB a day and PB per year from up-coming projects such as the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) and the Large Synoptic Survey Telescope (LSST), we are designing a benchmark for data-intensive astronomical workbenches. We specify the workload based on the type and size of the datasets, typical queries (like points-in-region, region-in-region, and cross-match queries), and other data-analysis tasks. We also define quantitative and qualitative metrics and consider different audiences. The proposed benchmark then allows scalability evaluation (How does my system scale?), and system comparison (How does X compare to Y?). Given the variety of astronomy data-intensive applications and use cases, it is unlikely to find a single best solution. By defining a benchmark, we can offer guidance for system designers to develop productive environments and for end-users to evaluate what environment works best for their personal analysis and research.
Kemper Ann
Nieto-Santisteban Maria
Scholl Tobias
Szalay Alex
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