Selectivity Estimation for String Predicates: Overcoming the Underestimation Problem

  • Authors:
  • Surajit Chaudhuri;Venkatesh Ganti;Luis Gravano

  • Affiliations:
  • -;-;-

  • Venue:
  • ICDE '04 Proceedings of the 20th International Conference on Data Engineering
  • Year:
  • 2004

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Abstract

Queries with (equality or LIKE) selection predicatesover string attributes are widely used in relationaldatabases. However, state-of-the-art techniques forestimating selectivities of string predicates are often biasedtowards severely underestimating selectivities. In thispaper, we develop accurate selectivity estimators for stringpredicates that adapt to data and query characteristics,and which can exploit and build on a variety of existingestimators. A thorough experimental evaluation over realdata sets demonstrates the resilience of our estimators tovariations in both data and query characteristics.