A relational database may not satisfy certain integrity constraints (ICs) for several reasons. However most likely most of the information in it is still consistent with the ICs. The answers to queries that are consistent with the ICs can be considered sematically correct answers, and are characterized [2] as ordinary answers that can be obtained from every minimally repaired version of the database. In this paper we address the problem of specifying those repaired versions as the minimal models of a theory written in Annotated Predicate Logic [27]. It is also shown how to specify database repairs using disjunctive logic program with annotation arguments and a classical stable model semantics.

A relational database is inconsistent if it does not satisfy a given set of integrity constraints. Nevertheless, it is likely that most of the data in it is consistent with the constraints. In this paper we apply logic programming based on answer sets to the problem of retrieving consistent information from a possibly inconsistent database. Since consistent information persists from the original database to every of its minimal repairs, the approach is based on a specification of database repairs using disjunctive logic programs with exceptions, whose answer set semantics can be represented and computed by systems that implement stable model semantics. These programs allow us to declare persistence by default of data from the original instance to the repairs; and changes to restore consistency, by exceptions. We concentrate mainly on logic programs for binary integrity constraints, among which we find most of the integrity constraints found in practice.

An intelligent agent may receive information about its environment from several different sources. How should the agent merge these items of information into a single, consistent piece? Taking our lead from the contraction + expansion approach to belief revision, we envisage a two-stage approach to this problem. The first stage consists of weakening the individual pieces of information into a form in which they can be consistently added together. The second, trivial, stage then consists of simply adding together the information thus obtained. This paper is devoted mainly to the first stage of this process, which we call social contraction. We consider both a postulational and a procedural approach to social contraction. The latter builds on the authorÕs framework of belief negotiation models. With the help of Spohn-type rankings we provide two possible instantiations of this extended framework. This leads to two interesting concrete families of social contraction functions. Ó 2005 Elsevier B.V. All rights reserved.

Abstract. We propose a generalization of the well-known Magic Sets technique to Datalog ¬ programs with (possibly unstratified) negation under stable model semantics. Our technique produces a new program whose evaluation is generally more efficient (due to a smaller instantiation), while preserving soundness under cautious reasoning. Importantly, if the original program is consistent, then full query-equivalence is guaranteed for both brave and cautious reasoning, which turn out to be sound and complete. In order to formally prove the correctness of our Magic Sets transformation, we introduce a novel notion of modularity for Datalog ¬ under the stable model semantics, which is relevant per se. We prove that a module can be evaluated independently from the rest of the program, while preserving soundness under cautious reasoning. For consistent programs, both soundness and completeness are guaranteed for brave reasoning and cautious reasoning as well. Our Magic Sets optimization constitutes an effective method for enhancing the performance of data-integration systems in which query-answering is carried out by means of cautious reasoning over Datalog ¬ programs. In fact, preliminary results of experiments in the EU project INFOMIX, show that Magic Sets are fundamental for the scalability of the system. 1

We consider herescalq aggregation queries in databases that mayviolzz a given set of functional dependencies. We de#ne consistent answers to such queries to begreatest-lEzqglzqgl upper bounds on thevalq of thescalW function acrossal (minimal repairs of the database. We show how to compute such answers. We provide acomplWg characterization of thecomputational compltati of thisproblz- Wealf show howtractabilfx can be improved inseveral special cases (oneinvolfz anovel applNgjfzz of Boyce--CoddNormal Form) and present apractical hybrid queryevalq###x method.

We address the problem of retrieving certain and consistent answers to queries posed to a mediated data integration system with open sources under the local-as-view paradigm using conjunctive and disjunctive view definitions. For obtaining certain answers a query program is run on top of a normal deductive database with choice that defines the class of minimal legal instances of the integration system under the cautious stable model semantics. This methodology works for all monotone Datalog queries. To compute answers to queries that are consistent wrt given global integrity constraints, the specification of minimal legal instances is combined with another disjunctive deductive database that specifies the repairs of those legal instances. This allows to retrieve the consistent answers to any Datalog query, for any set of universal and acyclic referential integrity constraints.

We address the problem of minimal-change integrity maintenance in the context of integrity constraints in relational databases. Using the framework proposed by Arenas, Bertossi, and Chomicki [4], we focus on two basic computational issues: repair checking (is a database instance a repair of a given database?) and consistent query answers (is a tuple an answer to a given query in every repair of a given database?). We study the computational complexity of both problems, delineating the boundary between the tractable and the intractable. We review relevant semantical issues and survey different computational mechanisms proposed in this context. Our analysis sheds light on the computational feasibility of minimal-change integrity maintenance. The tractable cases should lead to practical implementations. The intractability results highlight the inherent limitations of any integrity enforcement mechanism, e.g., triggers or referential constraint actions, as a way of performing minimal-change integrity maintenance.

Consistent answers from a relational database that violates a given set of integrity constraints are characterized [Arenas et al. 1999] as ordinary answers that can be obtained from every repaired version of the database. In this paper we address the problem of specifying the repairs of a database as the minimal models of a theory written in Annotated Predicate Logic [Kifer et al. 1992a]. The specification is then transformed into a disjunctive logic program with annotation arguments and a stable model semantics. From the program, consistent answers to first order queries are obtained.

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Abstract. We introduce a simple and practically efficient method for repairing inconsistent databases. The idea is to properly represent the underlying problem, and then use off-the-shelf applications for efficiently computing the corresponding solutions. Given a possibly inconsistent database, we represent the possible ways to restore its consistency in terms of signed formulae. Then we show how the ‘signed theory ’ that is obtained can be used by a variety of computational models for processing quantified Boolean formulae, or by constraint logic program solvers, in order to rapidly and efficiently compute desired solutions, i.e., consistent repairs of the database. 1