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Many repositories utilize the versatile RDF model to publish data.Repositories are typically distributed and geographically remote, but data areinterconnected (e.g., the Semantic Web) and queried globally by a language suchas SPARQL. Due to the network cost and the nature of the queries, the executiontime can be prohibitively high. Current solutions attempt to minimize thenetwork cost by redistributing all data in a preprocessing phase, but here aretwo drawbacks: (i) redistribution is based on heuristics that may not benefitmany of the future queries; and (ii) the preprocessing phase is very expensiveeven for moderate size datasets. In this paper we propose PHD-Store, a SPARQLengine for distributed RDF repositories. Our system does not assume anyparticular initial data placement and does not require prepartitioning; hence,it minimizes the startup cost. Initially, PHD-Store answers queries using apotentially slow distributed semi-join algorithm, but adapts dynamically to thequery load by incrementally redistributing frequently accessed data.Redistribution is done in a way that future queries can benefit from fasthash-based parallel execution. Our experiments with synthetic and real dataverify that PHD-Store scales to very large datasets; many repositories;converges to comparable or better quality of partitioning than existingmethods; and executes large query loads 1 to 2 orders of magnitude faster thanour competitors.

PHD-Store: An Adaptive SPARQL Engine with Dynamic Partitioning for Distributed RDF Repositories