Abstract. Peer-to-Peer systems have proven to be an effective way of sharing data. Modern protocols are able to efficiently route a message to a given peer. However, determining the destination peer in the first place is not always trivial. We propose a model in which peers advertise their expertise in the Peer-to-Peer network. The knowledge about the expertise of other peers forms a semantic topology. Based on the semantic similarity between the subject of a query and the expertise of other peers, a peer can select appropriate peers to forward queries to, instead of broadcasting the query or sending it to a random set of peers. To calculate our semantic similarity measure we make the simplifying assumption that the peers share the same ontology. We evaluate the model in a bibliographic scenario, where peers share bibliographic descriptions of publications among each other. In simulation experiments we show how expertise based peer selection improves the performance of a Peer-to-Peer system with respect to precision, recall and the number of messages.
Many Semantic Web problems are difficult to solve through common divide-and-conquer strategies, since they are hard to partition. We present Marvin, a parallel and distributed platform for processing large amounts of RDF data, on a network of loosely-coupled peers. We present our divide-conquer-swap strategy and show that this model converges towards completeness.Within this strategy, we address the problem of making distributed reasoning scalable and load-balanced. We present SpeedDate, a routing strategy that combines data clustering with random exchanges. The random exchanges ensure load balancing, while the data clustering attempts to maximise efficiency. SpeedDate is compared against random and deterministic (DHT-like) approaches, on performance and load-balancing. We simulate parameters such as system size, data distribution, churn rate, and network topology. The results indicate that SpeedDate is near-optimally balanced, performs in the same order of magnitude as a DHT-like approach, and has an average throughput per node that scales with √ i for i items in the system. We evaluate our overall Marvin system for performance, scalability, load balancing and efficiency.
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