HiBISCuS: Hypergraph-Based Source Selection for SPARQL Endpoint Federation

  • Muhammad Saleem
  • Axel-Cyrille Ngonga Ngomo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8465)


Efficient federated query processing is of significant importance to tame the large amount of data available on the Web of Data. Previous works have focused on generating optimized query execution plans for fast result retrieval. However, devising source selection approaches beyond triple pattern-wise source selection has not received much attention. This work presents HiBISCuS, a novel hypergraph-based source selection approach to federated SPARQL querying. Our approach can be directly combined with existing SPARQL query federation engines to achieve the same recall while querying fewer data sources. We extend three well-known SPARQL query federation engines with HiBISCus and compare our extensions with the original approaches on FedBench. Our evaluation shows that HiBISCuS can efficiently reduce the total number of sources selected without losing recall. Moreover, our approach significantly reduces the execution time of the selected engines on most of the benchmark queries.




Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Acosta, M., Vidal, M.-E., Lampo, T., Castillo, J., Ruckhaus, E.: ANAPSID: An adaptive query processing engine for SPARQL endpoints. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 18–34. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Akar, Z., Halaç, T.G., Ekinci, E.E., Dikenelli, O.: Querying the web of interlinked datasets using void descriptions. In: LDOW at WWW (2012)Google Scholar
  3. 3.
    Auer, S., Lehmann, J., Ngonga Ngomo, A.-C.: Introduction to linked data and its lifecycle on the web. In: Polleres, A., d’Amato, C., Arenas, M., Handschuh, S., Kroner, P., Ossowski, S., Patel-Schneider, P. (eds.) Reasoning Web 2011. LNCS, vol. 6848, pp. 1–75. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Görlitz, O., Staab, S.: Splendid: Sparql endpoint federation exploiting void descriptions. In: COLD at ISWC (2011)Google Scholar
  5. 5.
    Harth, A., Hose, K., Karnstedt, M., Polleres, A., Sattler, K.-U., Umbrich, J.: Data summaries for on-demand queries over linked data. In: WWW (2010)Google Scholar
  6. 6.
    Kaoudi, Z., Koubarakis, M., Kyzirakos, K.: Atlas: Storing, updating and querying rdf(s) data on top of dhts. JWS 8(4) (2010)Google Scholar
  7. 7.
    Ladwig, G., Tran, T.: Linked data query processing strategies. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part I. LNCS, vol. 6496, pp. 453–469. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Lynden, S., Kojima, I., Matono, A., Tanimura, Y.: ADERIS: An adaptive query processor for joining federated SPARQL endpoints. In: Meersman, R., et al. (eds.) OTM 2011, Part II. LNCS, vol. 7045, pp. 808–817. Springer, Heidelberg (2011)Google Scholar
  9. 9.
    Montoya, G., Vidal, M.-E., Acosta, M.: A heuristic-based approach for planning federated sparql queries. In: COLD (2012)Google Scholar
  10. 10.
    Quilitz, B., Leser, U.: Querying distributed RDF data sources with SPARQL. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 524–538. Springer, Heidelberg (2008)Google Scholar
  11. 11.
    Saleem, M., Ngonga Ngomo, A.-C., Xavier Parreira, J., Deus, H.F., Hauswirth, M.: DAW: Duplicate-aWare federated query processing over the web of data. In: Alani, H., et al. (eds.) ISWC 2013, Part I. LNCS, vol. 8218, pp. 574–590. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  12. 12.
    Saleem, M., Shanmukha, S., Ngonga, A.-C., Almeida, J.S., Decker, S., Deus, H.F.: Linked cancer genome atlas database. In: I-Semantics (2013)Google Scholar
  13. 13.
    Schmidt, M., Görlitz, O., Haase, P., Ladwig, G., Schwarte, A., Tran, T.: FedBench: A benchmark suite for federated semantic data query processing. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 585–600. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Schwarte, A., Haase, P., Hose, K., Schenkel, R., Schmidt, M.: FedX: Optimization techniques for federated query processing on linked data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 601–616. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  15. 15.
    Wang, X., Tiropanis, T., Davis, H.C.: Lhd: Optimising linked data query processing using parallelisation. In: LDOW at WWW (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Muhammad Saleem
    • 1
  • Axel-Cyrille Ngonga Ngomo
    • 1
  1. 1.IFI/AKSWUniversität LeipzigLeipzigGermany

Personalised recommendations