Advertisement

Bindings-Restricted Triple Pattern Fragments

  • Olaf Hartig
  • Carlos Buil-Aranda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10033)

Abstract

The Triple Pattern Fragment (TPF) interface is a recent proposal for reducing server load in Web-based approaches to execute SPARQL queries over public RDF datasets. The price for less overloaded servers is a higher client-side load and a substantial increase in network load (in terms of both the number of HTTP requests and data transfer). In this paper, we propose a slightly extended interface that allows clients to attach intermediate results to triple pattern requests. The response to such a request is expected to contain triples from the underlying dataset that do not only match the given triple pattern (as in the case of TPF), but that are guaranteed to contribute in a join with the given intermediate result. Our hypothesis is that a distributed query execution using this extended interface can reduce the network load (in comparison to a pure TPF-based query execution) without reducing the overall throughput of the client-server system significantly. Our main contribution in this paper is twofold: we empirically verify the hypothesis and provide an extensive experimental comparison of our proposal and TPF.

Keywords

Resource Description Framework Query Execution Triple Pattern Page Size Resource Description Framework Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Olaf Hartig’s work has been funded partially by the German Government, Federal Ministry of Education and Research under the project number 03WKCJ4D. Carlos Buil-Aranda’s work has been supported by the Millennium Nucleus Center for Semantic Web Research under Grant NC120004 and by UTFSM DGIP Project no. 116.24.1.

References

  1. 1.
    Acosta, M., Vidal, M.-E.: Networks of linked data eddies: an adaptive web query processing engine for RDF data. In: Marcelo, A., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 111–127. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-25007-6_7 CrossRefGoogle Scholar
  2. 2.
    Aluç, G., Hartig, O., Özsu, M.T., Daudjee, K.: Diversified stress testing of RDF data management systems. In: Mika, P., et al. (eds.) ISWC 2014, Part I. LNCS, vol. 8796, pp. 197–212. Springer, Heidelberg (2014)Google Scholar
  3. 3.
    Bizer, C., Eckert, K., Meusel, R., Mühleisen, H., Schuhmacher, M., Völker, J.: Deployment of RDFa, microdata, and microformats on the web – a quantitative analysis. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8219, pp. 17–32. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-41338-4_2 CrossRefGoogle Scholar
  4. 4.
    Buil-Aranda, C., Hogan, A., Umbrich, J., Vandenbussche, P.-Y.: SPARQL web-querying infrastructure: ready for action? In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8219, pp. 277–293. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-41338-4_18 CrossRefGoogle Scholar
  5. 5.
    Cyganiak, R., Wood, D., Lanthaler, M.: RDF 1.1 concepts and abstract syntax. W3C Recommendation, February 2014Google Scholar
  6. 6.
    Feigenbaum, L., Williams, G.T.: SPARQL protocol for RDF. W3C Recommendation (2013)Google Scholar
  7. 7.
    Fernández, J.D., Martínez-Prieto, M.A., Gutiérrez, C., Polleres, A., Arias, M.: Binary RDF representation for publication and exchange (HDT). Web Semant. Sci. Serv. Agents World Wide Web 19, 22–41 (2013)CrossRefGoogle Scholar
  8. 8.
    Haas, L.M., Kossmann, D., Wimmers, E.L., Yang, J.: Optimizing queries across diverse data sources. In: Proceedings of the 23rd International Conference on Very Large Data Bases (VLDB) (1997)Google Scholar
  9. 9.
    Harris, S., Seaborne, A.: SPARQL 1.1 query language. W3C Recommendation (2013)Google Scholar
  10. 10.
    Hartig, O., Bizer, C., Freytag, J.-C.: Executing SPARQL queries over the web of linked data. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 293–309. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Hartig, O., Buil-Aranda, C.: brTPF: bindings-restricted triple pattern fragments (Extended Version). CoRR abs/1608.08148 (2016)Google Scholar
  12. 12.
    Mika, P., Potter, T.: Metadata statistics for a large web corpus. In: Proceedings of the 5th Linked Data on the Web Workshop (LDOW) (2012)Google Scholar
  13. 13.
    Montoya, G., Skaf-Molli, H., Molli, P., Vidal, M.-E.: Federated SPARQL queries processing with replicated fragments. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 36–51. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-25007-6_3 CrossRefGoogle Scholar
  14. 14.
    Pérez, J., Arenas, M., Gutierrez, C.: Semantics and complexity of SPARQL. ACM Trans. Database Syst. 34(3), 16 (2009)CrossRefGoogle Scholar
  15. 15.
    Schmachtenberg, M., Bizer, C., Paulheim, H.: Adoption of the linked data best practices in different topical domains. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8796, pp. 245–260. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-11964-9_16 Google Scholar
  16. 16.
    Herwegen, J., Vocht, L., Verborgh, R., Mannens, E., Walle, R.: Substring filtering for low-cost linked data interfaces. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 128–143. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-25007-6_8 CrossRefGoogle Scholar
  17. 17.
    Van Herwegen, J., Verborgh, R., Mannens, E., Van de Walle, R.: Query execution optimization for clients of triple pattern fragments. In: Gandon, F., Sabou, M., Sack, H., d’Amato, C., Cudré-Mauroux, P., Zimmermann, A. (eds.) ESWC 2015. LNCS, vol. 9088, pp. 302–318. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  18. 18.
    Sande, M., Verborgh, R., Herwegen, J., Mannens, E., Walle, R.: Opportunistic linked data querying through approximate membership metadata. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 92–110. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-25007-6_6 CrossRefGoogle Scholar
  19. 19.
    Verborgh, R., et al.: Querying datasets on the web with high availability. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8796, pp. 180–196. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-11964-9_12 Google Scholar
  20. 20.
    Verborgh, R., Vander Sande, M., Hartig, O., Van Herwegen, J., De Vocht, L., De Meester, B., Haesendonck, G., Colpaert, P.: Triple pattern fragments: a low-cost knowledge graph interface for the web. J. Web Semant. 37–38, 184–206 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Hasso Plattner InstituteUniversity of PotsdamPotsdamGermany
  2. 2.Department of Computer and Information Science (IDA)Linköping UniversityLinköpingSweden
  3. 3.Informatics DepartmentUniversidad Técnica Federico Santa MaríaValparaísoChile

Personalised recommendations