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The Computer Journal 1999 42(2):100-111; doi:10.1093/comjnl/42.2.100
© 1999 by British Computer Society
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Tane: An Efficient Algorithm for Discovering Functional and Approximate Dependencies

Ykä HuhtalaA1, Juha KärkkäinenA1, Pasi PorkkaA1 and Hannu ToivonenA2

A1 Department of Computer Science, PO Box 26, FIN-00014 University of Helsinki, Finland A2 Rolf Nevanlinna Institute, PO Box 4, FIN-00014 University of Helsinki, Finland

The discovery of functional dependencies from relations is an important database analysis technique. We present TANE, an efficient algorithm for finding functional dependencies from large databases. TANE is based on partitioning the set of rows with respect to their attribute values, which makes testing the validity of functional dependencies fast even for a large number of tuples. The use of partitions also makes the discovery of approximate functional dependencies easy and efficient and the erroneous or exceptional rows can be identified easily. Experiments show that TANE is fast in practice. For benchmark databases the running times are improved by several orders of magnitude over previously published results. The algorithm is also applicable to much larger datasets than the previous methods.

Received 17 September, 1998. Revised 31 March, 1999.


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