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StarMR: an efficient star-decomposition based query processor for SPARQL basic graph patterns using MapReduce
conference contribution
posted on 2018-01-01, 00:00 authored by Qiang Xu, Xin Wang, Jianxin LiJianxin Li, Ying Gan, Lele Chai, Junhu WangWith 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 Open image in new window, 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 Open image in new window method outperforms the state-of-the-art method S2X by an order of magnitude.
History
Event
China Computer Federation. Conference (2nd : 2018 : Macau, China)Volume
10987Series
China Computer Federation ConferencePagination
415 - 430Publisher
SpringerLocation
Macau, ChinaPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2018-07-23End date
2018-07-25ISBN-13
978-3-319-96890-2Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2018, Springer International Publishing AG, part of Springer NatureEditor/Contributor(s)
Y Cai, Y Ishikawa, J XuTitle of proceedings
APWeb-WAIM 2018 : Proceedings of the Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big DataUsage metrics
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