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
2152959100 1527742398 2108930340 1501976648
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. In this paper we describe a new approach to repairing violations of integrity constraints in relational databases with null values. By adopting basic concepts from model-based diagnosis, we show how simultaneous reasons for violations of (different) constraints can be determined. These reasons, represented as sets of facts, directly indicate possible repair actions that guarantee to remove the observed violations. The authors address the issue of reasoning with two classes of commonly used semantic integrity constraints in database and knowledge-base systems: implication constraints and referential constraints. They first consider a central problem in this respect, the IRC-refuting problem, which is to decide whether a conjunctive query always produces an empty relation on (finite) database instances satisfying a given set of implication and referential constraints. Since the general problem is undecidable, they only consider acyclic referential constraints. Under this assumption, they prove that the IRC-refuting problem is decidable, and give a novel necessary and sufficient condition for it. Under the same assumption, they also study several other problems encountered in semantic query optimization, such as the semantics-based query containment problem, redundant join problem, and redundant selection-condition problem, and show that they are polynomially equivalent or reducible to the IRC-refuting problem. Moreover, they give results on reducing the complexity for some special cases of the IRC-refuting problem. We investigate the optimization of extended relational queries used in systems holding, for example, spatial, multimedia or constraint data. For such queries we must account for the built-in relations specific to the kind of data, and application dependent relationships between different relations. We show that the constraint database perspective and the use of constrained tuple-generating dependencies provides a general framework in which to address semantic query optimization for these queries. We establish some sufficient conditions for query transformations involving the introduction of relations, extending work in the literature for conventional databases. We introduce semantic query partition (SQP) as a useful technique for optimizing queries with expensive operations, and investigate the problem of generating subqueries, which is central to the use of SQP.