Abstract
This paper presents, for Frequent Query Discovery (FQD), an algorithm which employs a novel relation of equivalence in order to remove redundant queries in the output. An FQD algorithm returns a set of frequent queries from a data base of query transactions in Datalog formalism. A Datalog data base can represent complex structures, such as hyper graphs, and allows the use of background knowledge. Thus, it is useful in complex domains such as chemistry and bio-informatics. A conventional FQD algorithm, such as WARMR, checks the redundancy of the queries with a relation of equivalence based on the θ-subsumption, which results in discovering a large set of frequent queries. In this work, we reduce the set of frequent queries using another relation of equivalence based on relevance of a query with respect to a data base. The experiments with both real and artificial data sets show that our algorithm is faster than WARMR and the test of relevance can remove up to 92% of the frequent queries.
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Maloberti, J., Suzuki, E. (2003). Improving Efficiency of Frequent Query Discovery by Eliminating Non-relevant Candidates. In: Grieser, G., Tanaka, Y., Yamamoto, A. (eds) Discovery Science. DS 2003. Lecture Notes in Computer Science(), vol 2843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39644-4_19
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DOI: https://doi.org/10.1007/978-3-540-39644-4_19
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