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Improving SPARQL query performance with algebraic expression tree based caching and entity caching

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Abstract

To obtain comparable high query performance with relational databases, diverse database technologies have to be adapted to confront the complexity posed by both Resource Description Framework (RDF) data and SPARQL query. Database caching is one of such technologies that improves the performance of database with reasonable space expense based on the spatial/temporal/semantic locality principle. However, existing caching schemes exploited in RDF stores are found to be dysfunctional for complex query semantics. Although semantic caching approaches work effectively in this case, little work has been done in this area. In this paper, we try to improve SPARQL query performance with semantic caching approaches, i.e., SPARQL algebraic expression tree (AET) based caching and entity caching. Successive queries with multiple identical sub-queries and star-shaped joins can be efficiently evaluated with these two approaches. The approaches are implemented on a two-level-storage structure. The main memory stores the most frequently accessed cache items, and items swapped out are stored on the disk for future possible reuse. Evaluation results on three mainstream RDF benchmarks illustrate the effectiveness and efficiency of our approaches. Comparisons with previous research are also provided.

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Recommended reading

  • Dar, S., Franklin, M.J., Jónsson, B.T., Srivastava, D., Tan, M., 1996. Semantic Data Caching and Replacement. 22nd Int. Conf. on Very Large Data Bases, p.330–341.

  • Castillo, R., Leser, U., Rothe, C., 2010. RDFMatView: Indexing RDF Data for SPARQL Queries. Technical Report, Humboldt University.

  • Neumann, T., Weikum, G., 2008. RDF-3X: a RISC-style engine for RDF. Proc. VLDB Endow., 1(1):647–659.

    Google Scholar 

  • Martin, M., Unbehauen, J., Auer, S., 2010. Improving the performance of semantic Web applications with SPARQL query caching. LNCS, 6089:304–318. [doi:10.1007/978-3-642-13489-0_21]

    Google Scholar 

  • Broekstra, J., Kampman, A., van Harmelen, F., 2002. Sesame: a generic architecture for storing and querying RDF and RDF schema. LNCS, 2342:54–68. [doi:10.1007/3-540-48005-6_7]

    Google Scholar 

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Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, 110004, China

    Gang Wu

  2. MOE Key Laboratory of Medical Image Computing, Northeastern University, Shenyang, 110004, China

    Gang Wu

  3. School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China

    Meng-dong Yang

Authors
  1. Gang Wu
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  2. Meng-dong Yang
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Correspondence to Gang Wu.

Additional information

Project supported by the National Natural Science Foundation of China (Nos. 60903010, 61025007, and 60933001), the National Basic Research Program (973) of China (No. 2011CB302206), the Natural Science Foundation of Jiangsu Province, China (No. BK2009268), the Fundamental Research Funds for the Central Universities (No. N110404013), and the Key Laboratory of Advanced Information Science and Network Technology of Beijing (No. XDXX1011)

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Wu, G., Yang, Md. Improving SPARQL query performance with algebraic expression tree based caching and entity caching. J. Zhejiang Univ. - Sci. C 13, 281–294 (2012). https://doi.org/10.1631/jzus.C1101009

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  • DOI: https://doi.org/10.1631/jzus.C1101009

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