TY - JOUR
T1 - Metaqueries
T2 - Semantics, complexity, and efficient algorithms
AU - Ben-Eliyahu-Zohary, Rachel
AU - Gudes, Ehud
AU - Ianni, Giovambattista
N1 - Funding Information:
We would like to thank the anonymous referees for their thoughtful and detailed comments that helped improve the paper considerably. Many thanks to Arkady Shapiro for running the experiments and for providing useful comments on earlier drafts of this paper. We are grateful also to Fabrizio Angiulli who found a mistake in an earlier proof of Theorem 4.2. This research was supported by a grant from the Israeli Ministry of Science.
PY - 2003/9/1
Y1 - 2003/9/1
N2 - 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.
AB - 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.
KW - Data mining
KW - Knowledge discovery
KW - Metaqueries
KW - Support
UR - http://www.scopus.com/inward/record.url?scp=0042388782&partnerID=8YFLogxK
U2 - 10.1016/S0004-3702(03)00073-0
DO - 10.1016/S0004-3702(03)00073-0
M3 - Article
AN - SCOPUS:0042388782
SN - 0004-3702
VL - 149
SP - 61
EP - 87
JO - Artificial Intelligence
JF - Artificial Intelligence
IS - 1
ER -