Dynamic sample selection for approximate query processing

  • Authors:

  • Brian Babcock;Surajit Chaudhuri;Gautam Das

  • Affiliations:

  • Stanford University;Microsoft Research;Microsoft Research

  • Venue:
  • Proceedings of the 2003 ACM SIGMOD international conference on Management of data
  • Year:
  • 2003

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Abstract

In decision support applications, the ability to provide fast approximate answers to aggregation queries is desirable. One commonly-used technique for approximate query answering is sampling. For many aggregation queries, appropriately constructed biased (non-uniform) samples can provide more accurate approximations than a uniform sample. The optimal type of bias, however, varies from query to query. In this paper, we describe an approximate query processing technique that dynamically constructs an appropriately biased sample for each query by combining samples selected from a family of non-uniform samples that are constructed during a pre-processing phase. We show that dynamic selection of appropriate portions of previously constructed samples can provide more accurate approximate answers than static, non-adaptive usage of uniform or non-uniform samples.