A Graph Partitioning Approach to Distributed RDF Stores

With growing of Semantic Web data, especially RDF data, managing large RDF dataset on a single machine does not scale well. Previous work has explored how to distribute RDF triples to multiple machines. However due to inefficient dataset partitioning used by these solutions, the performance of distributed store system is significantly affected. In this paper, we proposed a promising approach that utilized the graph nature of RDF datasets to minimize relations between partitions after dataset partitioning, and optimized system design based on it. As shown in our experiments, our approach can effectively reduce communication cost of query-processing messages, balance size of partitions compared with other approaches, and enhance parallelism through independent sub-querying.