Towards efficient metaquerying

Rachel Ben-Eliyahu-Zohary, Ehud Gudes

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations

Abstract

Metaquery (also known as metapattem) is a datamining tool useful for learning rules involving more than one relation in the database. A metaquery is 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 metaqueries research in several ways. First, we analyze the related computational problem and classify it as NP-hard, with a tractable subset that is quite immediately evident. Second, we argue that the notion of support for metaqueries, where support is intuitively some indication to the relevance of the rules to be discovered, is not adequately defined in the literature, and propose our own definition. Third, we propose some efficient algorithms for computing support and present preliminary experimental results that indicate that our algorithms are indeed quite useful.

Original languageEnglish
Pages (from-to)800-805
Number of pages6
JournalIJCAI International Joint Conference on Artificial Intelligence
Volume2
StatePublished - 1 Dec 1999
Event16th International Joint Conference on Artificial Intelligence, IJCAI 1999 - Stockholm, Sweden
Duration: 31 Jul 19996 Aug 1999

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