Generalized Maximal Consistent Answers in P2P Deductive Databases

Abstract

The paper provides a contribution in computing consistent answers to logic queries in a P2P environment. Each peer joining a P2P system imports data from its neighbors by using a set of mapping rules, i.e. a set of semantic correspondences to a set of peers belonging to the same environment. By using mapping rules, as soon as it enters the system, a peer can participate and access all data available in its neighborhood, and through its neighborhood it becomes accessible to all the other peers in the system. The declarative semantics of a P2P system is defined in terms of minimal weak models. Under this semantics each peer uses its mapping rules to import minimal sets of mapping atoms allowing to satisfy its local integrity constraints. The contribution of the present paper consists in extending the classical notion of consistent answer by allowing the presence of partially defined atoms, i.e. atoms with “unknown” values due to the presence of tuples in different minimal weak models which disagree on the value of one or more attributes. The basic proposal is the following: in the presence of alternative minimal weak models the choice is to extracts the minimal consistent portion of the information they all hold, i.e. the information on which the minimal weak models agree. Therefore, true information is that “supported”’ by all minimal weak models, i.e. the set of atoms which maximizes the information shared by the minimal weak models.