Abstract
We assume that there is a deductive database 〈D, C〉, where D is a set of stratified rules and facts and C a set of constraints. Querying a database means returning all ground instances of the query which are true in the well defined standard model M D induced by D. Given a query Q it is transformed into a new query Q′, such that Q′ will be less costly to be evaluated than Q, and such that Q′ is guaranteed to return the same answer provided that the constraints and rules are satisfied by M D. This transformation is called Semantic Query Optimization (SQO). First we show four ways of transforming a query into a new one. This assures high flexibility for runtime SQO. Afterwards, the method is adapted in order to simplify (may be at compile time) the rules and constraints. The proposed simplifications are such that M D remains the same and such that the simplfied database is consistent iff the original one was. Henceforth, the inference process is heavily improved due to the simplified rules, and the work to be done to check the integrity is reduced since the constraints are simplified. Finally, we turn to query evaluation again and show when a rule may be ignored during the evaluation of a query.
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Wüthrich, B. (1991). Semantic improvement of deductive databases. In: Thalheim, B., Demetrovics, J., Gerhardt, H.D. (eds) MFDBS 91. MFDBS 1991. Lecture Notes in Computer Science, vol 495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-54009-1_16
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DOI: https://doi.org/10.1007/3-540-54009-1_16
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