PARIS: A peer-to-peer architecture for large-scale semantic data sharing

Today data sources are pervasive and their number is growing tremendously. Current tools are not able to exploit this unprecedented amount of information and to cope with this highly heterogeneous, autonomous and dynamic environment. In this paper, we propose a novel semantic overlay network architecture, PARIS, aimed at addressing these issues. In PARIS, the combination of decentralized semantic data integration with gossip-based (unstructured) overlay topology management and (structured) distributed hash tables provides the required level of flexibility, adaptability and scalability, and still allows to perform rich queries on a number of autonomous data sources. We describe the logical model that supports the architecture and show how its original topology is constructed. We present the usage of the system in detail, in particular, the algorithm used to execute queries on top of it and show simulation results that assess the scalability and robustness of the architecture.