Aggregates and priorities in P2P data management systems

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.