Metaqueries: Semantics, complexity, and efficient algorithms

Rachel Ben-Eliyahu-Zohary, Ehud Gudes, Giovambattista Ianni

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Metaquery (metapattern) is a data mining tool which is useful for learning rules involving more than one relation in the database. The notion of a metaquery has been proposed as a template or a second-order proposition in a language ℒ that describes the type of pattern to be discovered. This tool has already been successfully applied to several real-world applications. In this paper we advance the state of the art in metaquery research in several ways. First, we argue that the notion of a support value for metaqueries, where a support value is intuitively some indication to the relevance of the rules to be discovered, is not adequately defined in the literature, and, hence, propose our own definition. Second, we analyze some of the related computational problems, classify them as NP-hard and point out some tractable cases. Third, we propose some efficient algorithms for computing support and present preliminary experimental results that indicate the usefulness of our algorithms.

Original languageEnglish
Pages (from-to)61-87
Number of pages27
JournalArtificial Intelligence
Issue number1
StatePublished - 1 Sep 2003


  • Data mining
  • Knowledge discovery
  • Metaqueries
  • Support

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence


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