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.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (61572353), the National High-tech R&D Program of China (863 Program) (2013AA013204), and the Natural Science Foundation of Tianjin (17JCYBJC15400).
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Xu, Q., Wang, X., Li, J., Gan, Y., Chai, L., Wang, J. (2018). StarMR: An Efficient Star-Decomposition Based Query Processor for SPARQL Basic Graph Patterns Using MapReduce. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10987. Springer, Cham. https://doi.org/10.1007/978-3-319-96890-2_34
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