| Summary: | Web-scale RDF datasets are increasingly processed using distributed RDF data stores built on top of a cluster of shared-nothing servers. Such systems critically rely on their data partitioning scheme and query answering scheme, the goal of which is to facilitate correct and efficient query processing. Existing data partitioning schemes are commonly based on hashing or graph partitioning techniques. The latter techniques split a dataset in a way that minimises the number of connections between the resulting subsets, thus reducing the need for communication between servers; however, to facilitate efficient query answering, considerable duplication of data at the intersection between subsets is often needed. Building upon the known graph partitioning approaches, in this paper we present a novel data partitioning scheme that employs minimal duplication and keeps track of the connections between partition elements; moreover, we propose a query answering scheme that uses this additional information to correctly answer all queries. We show experimentally that, on certain well-known RDF benchmarks, our data partitioning scheme often allows more answers to be retrieved without distributed computation than the known schemes, and we show that our query answering scheme can efficiently answer many queries.
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