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 algorithmto 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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De Marchi, F., Lopes, S., Petit, JM. (2002). Efficient Algorithms for Mining Inclusion Dependencies. In: Jensen, C.S., et al. Advances in Database Technology — EDBT 2002. EDBT 2002. Lecture Notes in Computer Science, vol 2287. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45876-X_30
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DOI: https://doi.org/10.1007/3-540-45876-X_30
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