An Experimental Comparison of RDF Data Management Approaches in a SPARQL Benchmark Scenario

Efficient RDF data management is one of the cornerstones in realizing the Semantic Web vision. In the past, different RDF storage strategies have been proposed, ranging from simple triple stores to more advanced techniques like clustering or vertical partitioning on the predicates. The paper presents an experimental comparison of existing storage strategies on top of the SP2Bench SPARQL performance benchmark suite and put the results into context by comparing them to a purely relational model of the benchmark scenario. The author observes that in terms of performance and scalability, a simple triple store built on top of a column-store DBMS is competitive to the vertically partitioned approach when choosing a physical (predicate, subject, object) sort order.