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Efficient Algorithms for Mining Inclusion Dependencies

Abstract : Foreign keys form one of the most fundamental constraints for relational databases. Since they are not always defined in existing databases, algorithms need to be devised to discover foreign keys. One of the underlying problems is known to be the inclusion dependency (IND) inference problem. In this paper a new data mining algorithm for computing unary INDs is given. From unary INDs, we also propose a levelwise algorithm to discover all remaining INDs, where candidate INDs of size i + 1 are generated from satisfied INDs of size i; (i > 0). An implementation of these algorithms has been achieved and tested against synthetic databases. Up to our knowledge, this paper is the first one to address in a comprehensive manner this data mining problem, from algorithms to experimental results.
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Contributor : Stéphane Lopes <>
Submitted on : Tuesday, June 10, 2008 - 11:19:31 AM
Last modification on : Monday, January 20, 2020 - 12:12:05 PM


  • HAL Id : hal-00286653, version 1


Fabien de Marchi, Stéphane Lopes, Jean-Marc Petit. Efficient Algorithms for Mining Inclusion Dependencies. International Conference on Extending Database Technology (EDBT), 2002, Prague, Czech Republic. pp.464-476. ⟨hal-00286653⟩



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