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- research-articleNovember 2011
SciHadoop: array-based query processing in Hadoop
- Joe B. Buck,
- Noah Watkins,
- Jeff LeFevre,
- Kleoni Ioannidou,
- Carlos Maltzahn,
- Neoklis Polyzotis,
- Scott Brandt
SC '11: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and AnalysisArticle No.: 66, Pages 1–11https://doi.org/10.1145/2063384.2063473Hadoop has become the de facto platform for large-scale data analysis in commercial applications, and increasingly so in scientific applications. However, Hadoop's byte stream data model causes inefficiencies when used to process scientific data that is ...
- research-articleNovember 2011
ISABELA-QA: query-driven analytics with ISABELA-compressed extreme-scale scientific data
- Sriram Lakshminarasimhan,
- John Jenkins,
- Isha Arkatkar,
- Zhenhuan Gong,
- Hemanth Kolla,
- Seung-Hoe Ku,
- Stephane Ethier,
- Jackie Chen,
- C. S. Chang,
- Scott Klasky,
- Robert Latham,
- Robert Ross,
- Nagiza F. Samatova
SC '11: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and AnalysisArticle No.: 31, Pages 1–11https://doi.org/10.1145/2063384.2063425Efficient analytics of scientific data from extreme-scale simulations is quickly becoming a top-notch priority. The increasing simulation output data sizes demand for a paradigm shift in how analytics is conducted. In this paper, we argue that query-...
- research-articleNovember 2011
I/O streaming evaluation of batch queries for data-intensive computational turbulence
SC '11: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and AnalysisArticle No.: 29, Pages 1–10https://doi.org/10.1145/2063384.2063423We describe a method for evaluating computational turbulence queries, including Lagrange Polynomial interpolation, based on partial sums that allows the underlying data to be accessed in any order and in parts. We exploit these properties to stream data ...