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TANE : An efficient algorithm for discovering functional and approximate dependencies

HUHTALA, Y ; KÄRKKÄINEN, J ; PORKKA, P ; TOIVONEN, H

Computer journal, 1999-01, Vol.42 (2), p.100-111 [Periódico revisado por pares]

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Título:
TANE : An efficient algorithm for discovering functional and approximate dependencies
Autor: HUHTALA, Y ; KÄRKKÄINEN, J ; PORKKA, P ; TOIVONEN, H
Assuntos: Applied sciences ; Computer science; control theory; systems ; Data mining ; Exact sciences and technology ; Information systems. Data bases ; Memory organisation. Data processing ; Software
É parte de: Computer journal, 1999-01, Vol.42 (2), p.100-111
Notas: ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
Descrição: 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.
Editor: Oxford: Oxford University Press
Idioma: Inglês

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