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<p>The authors present an approach to acquiring knowledge from previously processed queries. By using newly acquired knowledge together with given semantic knowledge, it is possible to make the query processor and/or optimizer more intelligent so that future queries can b processed more efficiently. The acquired knowledge is in the form of constraints. While some constraints are to be enforced for all database states, others are known to be valid for the current state of the database. The former constraints are statistic integrity constraints, while the latter are called dynamic integrity constraints. Some situations in which certain dynamic semantic constraints can be automatically extracted are identified. This automatic tool for knowledge acquisition can also be used as an interactive tool for identifying potential static integrity constraints. The concept of minimal knowledge base is introduced, and a method to maintain the knowledge base is presented. An algorithm to compute the restriction (selection) closure, i.e. all deductible restrictions, from a given set of restrictions, join predicates (as given in a query), and constraints is given.</p>
maintenance; semantic query optimization; constraints; database states; statistic integrity constraints; dynamic integrity constraints; automatic tool; knowledge acquisition; interactive tool; join predicates; database management systems; knowledge acquisition; knowledge based systems

W. Sun and C. Yu, "Automatic Knowledge Acquisition and Maintenance for Semantic Query Optimization," in IEEE Transactions on Knowledge & Data Engineering, vol. 1, no. , pp. 362-375, 1989.
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