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ERA-RJN: A SPARQL-Rank Based Top-k Join Query Optimization

  • Zhengrong XiaoEmail author
  • Fengjiao Chen
  • Fangfang Xu
  • Jinguang Gu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9544)

Abstract

With the wide use of RDF data, searching and ranking semantic data with SPARQL has become a research hot-spot. While there is no much research work on top-k join queries optimization in RDF native stores. This paper proposes a new rank-join operator algorithm ERA-RJN on the basis of SPARQL-RANK algebra, making use of the advantage of random access availability in RDF native storage. This paper implements the ERA-RJN operator on the ARQ-RANK platform, and performs experiments, verifies the high efficiency of ERA-RJN algorithm dealing with SPARQL top-k join query in RDF native storage .

Keywords

RDF native storage Top-k join Rank-join operator SPARQL-RANK 

Notes

Acknowledgements

This topic research is done under the guidance of Professor JinGuang Gu. Avail ourselves of this opportunity to express heartfelt thanks to Professor Gu. And student Fangfang Xu, YuWei Zou helped with some technical stuff. This work is supported by the Natural Science Foundation Project of Hubei Province (2013CFB334), the Scientific Research Project of Hubei Provincial Education Department (Q20101110, D2009110) and the Outstanding Young Scientific and Technological Innovation Team Project of Hubei Provincial Colleges and Universities (T201202), much thanks with it.

References

  1. 1.
    Ilyas, I.F., Beskales, G., Soliman, M.A.: A survey of top-k query processing techniques in relational database systems. ACM Trans. Comput. Surv. 40(4), 198–205 (2008)Google Scholar
  2. 2.
    Decker, S., Melnik, S., Van Harmelen, F., et al.: The semantic web: the roles of XML and RDF. Internet Comput. IEEE 4(5), 63–73 (2000)CrossRefGoogle Scholar
  3. 3.
    Perez, J., Arenas, M., Gutierrez, C.: Semantics and complexity of SPARQL. ACM Trans. Database Syst. 34(3), 341–352 (2009)CrossRefGoogle Scholar
  4. 4.
    Schmidt M, Meier M, Lausen G. Foundations of SPARQL query optimization. In: Proceedings of the 13th International Conference on Database Theory, pp. 4–33. ACM (2010)Google Scholar
  5. 5.
    Nikolov, A., Schwarte, A., Hütter, C.: FedSearch: efficiently combining structured queries and full-text search in a SPARQL Federation. In: Alani, H., Kagal, L., Fokoue, A., Groth, P., Biemann, C., Parreira, J.X., Aroyo, L., Noy, N., et al. (eds.) ISWC 2013, Part I. LNCS, vol. 8218, pp. 427–443. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Bozzon, A., Della Valle, E., Magliacane, S.: Extending SPARQL algebra to support efficient evaluation of top-k SPARQL queries. In: Ceri, S., Brambilla, M. (eds.) Search Computing: Broadening Web Search. LNCS, vol. 7538, pp. 143–156. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Liu, J., Feng, L., Xing, Y.: A pruning-based approach for supporting top-k join queries. In: Proceedings of the 15th International Conference on World Wide Web, pp. 891–892. ACM (2006)Google Scholar
  8. 8.
    Martinenghi, D., Tagliasacchi, M.: Proximity measures for rank join. ACM Trans. Database Syst. (TODS) 37(1), 1–45 (2012)CrossRefGoogle Scholar
  9. 9.
    Li, C., Chen-Chuan, K., Ihab, C., et al.: RankSQL: query algebra and optimization for relational top-k queries. In: SIGMOD, pp. 131–142 (2005)Google Scholar
  10. 10.
    Magliacane, S., Bozzon, A., Della Valle, E.: Efficient execution of top-k SPARQL queries. In: Cudré-Mauroux, P., Heflin, J., Sirin, E., Tudorache, T., Euzenat, J., Hauswirth, M., Parreira, J.X., Hendler, J., Schreiber, G., Bernstein, A., Blomqvist, E. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 344–360. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    Ilyas, I.F., Aref, W.G., Elmagarmid, A.K.: Supporting top-k join queries in relational databases. VLDB J.—Int. J. Very Large Data Bases 13(3), 207–221 (2004)Google Scholar
  12. 12.
    Martinenghi, D., Tagliasacchi, M.: Cost-aware rank join with random and sorted access. IEEE Trans. Knowl. Data Eng. 24(12), 2143–2155 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zhengrong Xiao
    • 1
    • 2
    Email author
  • Fengjiao Chen
    • 1
    • 2
  • Fangfang Xu
    • 1
    • 2
  • Jinguang Gu
    • 1
    • 2
  1. 1.College of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanChina
  2. 2.Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial SystemWuhanChina

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