Consistent Data Integration in P2P Deductive Databases

Abstract

Data Integration in Peer-to-Peer (P2P) systems is concerned with the ability of physically connect autonomous sources (peer) for sharing and reuse information and for the creation of new information from existing one. In a P2P system a query can be posed to any peer and the answer is provided by integrating locally stored data with data provided from its neighbors. Anyhow, while collecting data for answering queries, imported data may corrupt the local database due to the violation of some integrity constraint, therefore inconsistencies have to be managed. This paper contributes to the proposal of a logic based framework for data integration and query answering in a Peer-to-Peer environment. It is based on [11,12] in which the Preferred Weak Model Semantics, capturing a different perspective for P2P data integration, has been proposed: just data not violating integrity constraints are exchanged among peers by using mapping rules. The motivation of this work stems from the observation that the complexity of computing preferred weak models in [11,12] does not let the approach to be implemented in practical applications. Therefore, a more pragmatic solution seems to be desirable for assigning semantics to a P2P system. First, the paper proposes a rewriting technique that allows modeling a P2P system, \({\mathcal {PS}}\), as a unique logic program, Rew t (\({\mathcal {PS}}\)), whose stable models correspond to the preferred weak models of \({\mathcal {PS}}\). Then, it presents the Well Founded Model Semantics, that allows obtaining a deterministic model whose computation is polynomial time. This is a (partial) stable model obtained by evaluating with a three-value semantics a logic program obtained from Rew t (\({\mathcal {PS}})\). Finally, the paper provides results on the complexity of answering queries in a P2P system.KeywordsLogic ProgramStable ModelMapping AtomModel SemanticIntegrity ConstraintThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.