OPTIMIZING ACQUAINTANCE SELECTION IN A PDMS

In a Peer Data Management System (PDMS), autonomous peers share semantically rich data. For queries to be translated across peers, a peer must provide a mapping to other peers in the PDMS; peers connected by such mappings are called acquaintances. To maximize PDMS query answering performance, a peer needs to optimize its choice of acquaintances. This paper investigates the acquaintance selection problem and introduces a novel framework for performing this acquaintance selection. Our framework includes two selection schemes that effectively and efficiently estimate mapping effectiveness. The "one-shot" scheme clusters peers and estimates the improvement in query answering based on cluster properties. The "two-hop" scheme estimates using locally available information at multiple rounds. Our empirical study shows that both schemes effectively help acquaintance selection and scale to large PDMSs.