Supporting large-scale computational science

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Description

Business needs have driven the development of commercial database systems since their inception. As a result, there has been a strong focus on supporting many users, minimizing the potential corruption or loss of data, and maximizing performance metrics like transactions per second, or TPC-C and TPC-D results. It turns out that these optimizations have little to do with the needs of the scientific community, and in particular have little impact on improving the management and use of large-scale high-dimensional data. At the same time, there is an unanswered need in the scientific community for many of the benefits offered by ... continued below

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19 p.

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Musick, R. February 19, 1998.

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Description

Business needs have driven the development of commercial database systems since their inception. As a result, there has been a strong focus on supporting many users, minimizing the potential corruption or loss of data, and maximizing performance metrics like transactions per second, or TPC-C and TPC-D results. It turns out that these optimizations have little to do with the needs of the scientific community, and in particular have little impact on improving the management and use of large-scale high-dimensional data. At the same time, there is an unanswered need in the scientific community for many of the benefits offered by a robust DBMS. For example, tying an ad-hoc query language such as SQL together with a visualization toolkit would be a powerful enhancement to current capabilities. Unfortunately, there has been little emphasis or discussion in the VLDB community on this mismatch over the last decade. The goal of the paper is to identify the specific issues that need to be resolved before large-scale scientific applications can make use of DBMS products. This topic is addressed in the context of an evaluation of commercial DBMS technology applied to the exploration of data generated by the Department of Energy`s Accelerated Strategic Computing Initiative (ASCI). The paper describes the data being generated for ASCI as well as current capabilities for interacting with and exploring this data. The attraction of applying standard DBMS technology to this domain is discussed, as well as the technical and business issues that currently make this an infeasible solution.

Physical Description

19 p.

Notes

OSTI as DE98058635

Other: FDE: PDF; PL:

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  • Very large databases conference, New York, NY (United States), 24-27 Aug 1998

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  • Other: DE98058635
  • Report No.: UCRL-JC--129903
  • Report No.: CONF-980838--
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 314880
  • Archival Resource Key: ark:/67531/metadc688830

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  • February 19, 1998

Added to The UNT Digital Library

  • July 25, 2015, 2:20 a.m.

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  • April 10, 2017, 2:32 p.m.

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Musick, R. Supporting large-scale computational science, article, February 19, 1998; California. (digital.library.unt.edu/ark:/67531/metadc688830/: accessed September 20, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.