Scientific data management in the coming decade

  • Authors:
  • Jim Gray;David T. Liu;Maria Nieto-Santisteban;Alex Szalay;David J. DeWitt;Gerd Heber

  • Affiliations:
  • Microsoft;Berkeley;Johns Hopkins University;Johns Hopkins University;Wisconsin;Cornell

  • Venue:
  • ACM SIGMOD Record
  • Year:
  • 2005

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Abstract

Scientific instruments and computer simulations are creating vast data stores that require new scientific methods to analyze and organize the data. Data volumes are approximately doubling each year. Since these new instruments have extraordinary precision, the data quality is also rapidly improving. Analyzing this data to find the subtle effects missed by previous studies requires algorithms that can simultaneously deal with huge datasets and that can find very subtle effects --- finding both needles in the haystack and finding very small haystacks that were undetected in previous measurements.