SPARQL Web-Querying Infrastructure: Ready for Action?

  • Carlos Buil-Aranda
  • Aidan Hogan
  • Jürgen Umbrich
  • Pierre-Yves Vandenbussche
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8219)

Abstract

Hundreds of public SPARQL endpoints have been deployed on the Web, forming a novel decentralised infrastructure for querying billions of structured facts from a variety of sources on a plethora of topics. But is this infrastructure mature enough to support applications? For 427 public SPARQL endpoints registered on the DataHub, we conduct various experiments to test their maturity. Regarding discoverability, we find that only one-third of endpoints make descriptive meta-data available, making it difficult to locate or learn about their content and capabilities. Regarding interoperability, we find patchy support for established SPARQL features like ORDER BY as well as (understandably) for new SPARQL 1.1 features. Regarding efficiency, we show that the performance of endpoints for generic queries can vary by up to 3–4 orders of magnitude. Regarding availability, based on a 27-month long monitoring experiment, we show that only 32.2% of public endpoints can be expected to have (monthly) “two-nines” uptimes of 99–100%.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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.
    Alexander, K., Cyganiak, R., Hausenblas, M., Zhao, J.: Describing linked datasets. In: LDOW, vol. 538. CEUR (2009)Google Scholar
  3. 3.
    Basca, C., Bernstein, A.: Avalanche: Putting the spirit of the web back into semantic web querying. In: SSWS, vol. 669, pp. 524–538. CEUR (2010)Google Scholar
  4. 4.
    Bornhövd, C., Altinel, M., Mohan, C., Pirahesh, H., Reinwald, B.: Adaptive database caching with dbcache. IEEE Data Eng. Bull. 27(2), 11–18 (2004)Google Scholar
  5. 5.
    Buil-Aranda, C., Arenas, M., Corcho, O., Polleres, A.: Federating queries in SPARQL 1.1: Syntax, semantics and evaluation. JWS 18(1), 1–17 (2013)CrossRefGoogle Scholar
  6. 6.
    Clark, K.G., Feigenbaum, L., Torres, E.: SPARQL Protocol for RDF. W3C Recommendation (January 2008)Google Scholar
  7. 7.
    Franklin, M.J., Halevy, A.Y., Maier, D.: From databases to dataspaces: a new abstraction for information management. SIGMOD Record 34(4), 27–33 (2005)CrossRefGoogle Scholar
  8. 8.
    Gallego, M.A., Fernández, J.D., Martínez-Prieto, M.A., Fuente, P.D.L.: An empirical study of real-world SPARQL queries. In: USEWOD Workshop (2012)Google Scholar
  9. 9.
    Glaser, H., Millard, I.C., Jaffri, A.: RKBExplorer.com: A knowledge driven infrastructure for Linked Data providers. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 797–801. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Goldman, R., Widom, J.: DataGuides: Enabling Query Formulation and Optimization in Semistructured Databases. In: VLDB, pp. 436–445 (1997)Google Scholar
  11. 11.
    Görlitz, O., Staab, S.: SPLENDID: SPARQL endpoint federation exploiting VOID descriptions. In: COLD, vol. 782. CEUR (2011)Google Scholar
  12. 12.
    Harris, S., Seaborne, A.: SPARQL 1.1 query language. W3C Recommendation (March 2013)Google Scholar
  13. 13.
    Hogan, A., Umbrich, J., Harth, A., Cyganiak, R., Polleres, A., Decker, S.: An empirical survey of Linked Data conformance. JWS 14, 14–44 (2012)CrossRefGoogle Scholar
  14. 14.
    Janev, V., Vraneš, S.: Applicability assessment of semantic web technologies. Information Processing & Management 47(4), 507–517 (2011)CrossRefGoogle Scholar
  15. 15.
    Koloniari, G., Pitoura, E.: Content-based routing of path queries in peer-to-peer systems. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 29–47. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  16. 16.
    Langegger, A., Wöß, W., Blöchl, M.: A Semantic Web middleware for virtual data integration on the Web. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 493–507. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Morsey, M., Lehmann, J., Auer, S., Ngomo, A.-C.N.: DBpedia SPARQL benchmark – performance assessment with real queries on real 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. 454–469. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  18. 18.
    Prud’hommeaux, E., Seaborne, A.: SPARQL Query Language for RDF. W3C Recommendation (January 2008)Google Scholar
  19. 19.
    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)CrossRefGoogle Scholar
  20. 20.
    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
  21. 21.
    Williams, G.T.: SPARQL 1.1 Service Description. W3C Recommendation (March 2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Carlos Buil-Aranda
    • 1
  • Aidan Hogan
    • Jürgen Umbrich
      • 2
    • Pierre-Yves Vandenbussche
      • 2
    1. 1.Department of Computer SciencePontificia Universidad Católica de ChileChile
    2. 2.Fujitsu (Ireland) Limited, Swords, Co.DublinIreland

    Personalised recommendations