Classical algorithms for query optimization presuppose the absence of inconsistencies or uncertainties in the database and exploit only valid semantic knowledge provided, e.g., by integrity constraints. Data inconsistency or uncertainty, however, is a widespread critical issue in ordinary databases: total integrity is often, in fact, an unrealistic assumption and violations to integrity constraints may be introduced in several ways. In this report we present an approach for semantic query optimization that, differently from the traditional ones, relies on not necessarily valid semantic knowledge, e.g., provided by violated or soft integrity constraints, or induced by applying data mining techniques. Query optimization that leverages invalid semantic knowledge cannot guarantee the semantic equivalence between the original user's query and its rewriting: thus a query optimized by our approach yields approximate answers that can be provided to the users whenever fast but possibly partial responses are required. Also, we evaluate the impact of use of invalid semantic knowledge in the rewriting of a query by computing a measure of the quality of the answer returned to the user, and we rely on the recent theory of Belief Logic Programming to deal with the presence of possible correlation in the semantic knowledge used in the rewriting.
Another classical application field of integrity constraints, besides SQO, is cooperative query answering @cite_10 , where however the constraints are used only as long as they are valid.
@cite_10
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Recently there has been an increasing interest in integrity constraints associated with relational databases and in inconsistent databases, i.e. databases which do not satisfy integrity constraints. In the presence of inconsistencies two main techniques have been proposed: compute repairs, i.e. minimal set of insertion and deletion operations, called database repairs, and compute consistent answers, i.e. identify the sets of atoms which we can assume true, false and undefined without modifying the database. In this paper feasibility conditions and preference criteria are introduced which, associated with integrity constraints, allow to restrict the number of repairs and to increase the power of queries over inconsistent databases. Moreover, it is studied the complexity of computing repairs and the expressive power of relational queries over databases with integrity constraints, feasibility conditions and preference criteria.