StarMR: An Efficient Star-Decomposition Based Query Processor for SPARQL Basic Graph Patterns Using MapReduce

Qiang Xu, Xin Wang, Jianxin Li, Ying Gan, Lele Chai, Junhu Wang

Abstract: With the proliferation of knowledge graphs, large amounts of RDF graphs have been released, which raises the need for addressing the challenge of distributed SPARQL queries. In this paper, we propose an efficient distributed method, called , to answer the SPARQL basic graph pattern (BGP) queries on big RDF graphs using MapReduce. In our method, query graphs are decomposed into a set of stars that utilize the semantic and structural information embedded RDF graphs as heuristics. Two optimization techniques are proposed to further improve the efficiency of our algorithms. One filters out invalid input data, the other postpones the Cartesian product operations. The extensive experiments on both synthetic and real-world datasets show that our method outperforms the state-of-the-art method S2X by an order of magnitude.