A Framework for Collaborative Updates in Selective Data Replication Communities

Abstract

Collaborative replication updates are an attractive property of Selective Data Replication in which data consumers cooperate to update their data set replications. Data consumer communities are implemented as groups of intelligent software agents who make decisions about when updates should occur. The software agent paradigm is suitable for achieving collaboration between individual agents, however some structured collaboration model must be followed. The theory of Cooperative Problem Solving (CPS) [19] describes a theoretical model for achieving collaboration between a group of software agents. We present a framework implemented using the OPAL agent platform, and derived from the CPS theory, for achieving collaborative replication updates within a community of data consumers.