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gStore: a graph-based SPARQL query engine

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Abstract

We address efficient processing of SPARQL queries over RDF datasets. The proposed techniques, incorporated into the gStore system, handle, in a uniform and scalable manner, SPARQL queries with wildcards and aggregate operators over dynamic RDF datasets. Our approach is graph based. We store RDF data as a large graph and also represent a SPARQL query as a query graph. Thus, the query answering problem is converted into a subgraph matching problem. To achieve efficient and scalable query processing, we develop an index, together with effective pruning rules and efficient search algorithms. We propose techniques that use this infrastructure to answer aggregation queries. We also propose an effective maintenance algorithm to handle online updates over RDF repositories. Extensive experiments confirm the efficiency and effectiveness of our solutions.

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Notes

  1. The literature on aggregation and aggregate queries frequently refer to these attributes as dimensions. We follow the same convention.

  2. Note that, this is not necessarily \(O_n\), since node identifiers are arbitrarily assigned to help with presentation.

  3. Although dimension list is a set, when the order is important, we specify them as a list enclosed in ( ).

  4. We revised the original 14 to remove type reasoning that gStore does not currently support; the resulting queries return larger result sets since there is no filtering as a result of type reasoning. For completeness, these are included in Online Supplements as Table 15.

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Acknowledgments

Lei Zou’s work was supported by National Science Foundation of China (NSFC) under Grant No. 61370055 and by CCF-Tencent Open Research Fund. M. Tamer Özsu’s work was supported by Natural Sciences and Engineering Research Council (NSERC) of Canada under a Discovery Grant. Lei Chen’s work was supported in part by the Hong Kong RGC Project M-HKUST602/12, National Grand Fundamental Research 973 Program of China under Grant No. 2012-CB316200, Microsoft Research Asia Grant, and a Google Faculty Award. Dongyan Zhao was supported by NSFC under Grant No. 61272344 and China 863 Project under Grant No. 2012AA011101.

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Authors and Affiliations

  1. Institute of Computer Science and Technology, Peking University, Beijing, China

    Lei Zou, Xuchuan Shen, Ruizhe Huang & Dongyan Zhao

  2. David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada

    M. Tamer Özsu

  3. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China

    Lei Chen

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  1. Lei Zou
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  2. M. Tamer Özsu
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  4. Xuchuan Shen
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Correspondence to M. Tamer Özsu.

Additional information

Extended version of paper “gStore: Answering SPARQL Queries via Subgraph Matching” that was presented at 2011 VLDB Conference.

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Zou, L., Özsu, M.T., Chen, L. et al. gStore: a graph-based SPARQL query engine. The VLDB Journal 23, 565–590 (2014). https://doi.org/10.1007/s00778-013-0337-7

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