This paper presents a logic framework for modeling the interaction among deductive databases in a P2P (Peer to Peer) environment. Each peer joining a P2P system provides or 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. Two different types of mapping rules are defined: mapping rules allowing to import a maximal set of atoms not leading to inconsistency (called maximal mapping rules) and mapping rules allowing to import a minimal set of atoms needed to restore consistency (called minimal mapping rules). Implicitly, the use of maximal mapping rules states it is preferable to import as long as no inconsistencies arise; whereas the use of minimal mapping rules states that it is preferable not to import unless a inconsistency exists. The paper presents three different declarative semantics of a P2P system: (i) the Max Weak Model Semantics, in which mapping rules are used to import as much knowledge as possible from a peer's neighborhood without violating local integrity constraints; (ii) the Min Weak Model Semantics, in which the P2P system can be locally inconsistent and the information provided by the neighbors is used to restore consistency, that is to only integrate the missing portion of a correct, but incomplete database; (iii) the Max-Min Weak Model Semantics that unifies the previous two different perspectives captured by the Max Weak Model Semantics and Min Weak Model Semantics. This last semantics allows to characterize each peer in the neighborhood as a resource used either to enrich (integrate) or to fix (repair) the knowledge, so as to define a kind of integrate-repair strategy for each peer. Under consideration in Theory and Practice of Logic Programming (TPLP).
In a more general perspective, interesting semantics for data exchange systems, that offer the possibility of explicitly modeling some preference criteria while performing the data integration process, has been proposed in @cite_32 @cite_39 @cite_55 @cite_20 @cite_7 . In @cite_32 @cite_39 @cite_55 a semantics is proposed that allows for cooperation among pairwise peers that are related to each other by means of data exchange constraints (i.e. mapping rules) and trust relationships. The decision by a peer on what other data to consider (besides its local data) does not depend only on its data exchange constraints, but also on the trust relationship that it has with other peers. Given a peer @math in a P2P system a solution for @math is a database instance that respects the exchange constraints and trust relationship @math has with its immediate neighbors'. Trust relationships are of the form: @math stating that @math trusts itself less that @math , @math stating that @math trusts itself more that @math and @math stating that @math trusts itself the same as @math . These trust relationships are static and are used in the process of collecting data in order to establish preferences in the case of conflicting information.
@cite_7 @cite_55 @cite_32 @cite_39 @cite_20
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This paper investigates the data exchange problem among distributed independent sources. It is based on previous works of the authors [11, 12, 14] in which a declarative semantics for P2P systems has been presented and a mechanism to set different degrees of reliability for neighbor peers has been provided. The basic semantics for P2P systems defines the concept of Maximal Weak Models (in [11, 12, 14] these models have been called Preferred Weak Models. In this paper we rename them and use the term Preferred for the subclass of Weak Model defined here) that represent scenarios in which maximal sets of facts not violating integrity constraints are imported into the peers [11, 12]. Previous priority mechanism defined in [14] is rigid in the sense that the preference between conflicting sets of atoms that a peer can import only depends on the priorities associated to the source peers at design time. In this paper we present a different framework that allows to select among different scenarios looking at the properties of data provided by the peers. The framework presented here allows to model concepts like "in the case of conflicting information, it is preferable to import data from the neighbor peer that can provide the maximum number of tuples" or "in the case of conflicting information, it is preferable to import data from the neighbor peer such that the sum of the values of an attribute is minimum" without selecting a-priori preferred peers. To enforce this preference mechanism we enrich the previous P2P framework with aggregate functions and present significant examples showing the flexibility of the new framework. We propose and investigate a semantics for peer data exchange systems (or peer data management systems) where different peers are pairwise related to each other by means of data exchange constraints and trust relationships. These two elements plus the data at the peers' sites and the local integrity constraints for a peer are made compatible via the proposed semantics by determining a set of solution instances, which are the intended virtual instances for the peer. The semantically correct answers from a peer to a query, called its peer consistent answers, are defined as those answers that are invariant under all its different solution instances. We show that solution instances can be specified as the models of logic programs with a stable model semantics. The problem of answering queries posed to a peer who is a member of a peer-to-peer data exchange system is studied. The answers have to be consistent wrt to both the local semantic constraints and the data exchange constraints with other peers; and must also respect certain trust relationships between peers. A semantics for peer consistent answers under exchange constraints and trust relationships is introduced and some techniques for obtaining those answers are presented. This paper investigates the data exchange problem among distributed independent sources. It is based on previous works in [9,10] in which a (declarative) semantics for P2P systems. In this semantics only facts not making the local databases inconsistent are imported Weak Models, and the Preferred Weak Models are those in which peers import maximal sets of facts not violating integrity constraints. The framework proposed in [9,10] does not provide any mechanism to set priorities among mapping rules. Anyhow, while collecting data it is quite natural for a source peer to associate different degrees of reliability to the portion of data provided by its neighbor peers. Starting from this observation, this paper enhances previous semantics by using priority levels among mapping rules in order to select the weak models containing a maximum number of mapping atoms according to their importance. We will call these weak models, Trusted Weak Models and we will show they can be computed as stable models of a logic program with weak constraints.