Enabling Fine-Grained HTTP Caching of SPARQL Query Results

  • Gregory Todd Williams
  • Jesse Weaver
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7031)


As SPARQL endpoints are increasingly used to serve linked data, their ability to scale becomes crucial. Although much work has been done to improve query evaluation, little has been done to take advantage of caching. Effective solutions for caching query results can improve scalability by reducing latency, network IO, and CPU overhead. We show that simple augmentation of the database indexes found in common SPARQL implementations can directly lead to effective caching at the HTTP protocol level. Using tests from the Berlin SPARQL benchmark, we evaluate the potential of such caching to improve overall efficiency of SPARQL query evaluation.


Search Tree Query Result Access Pattern Graph Pattern SPARQL Query 
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.


  1. 1.
    Breslau, L., Cao, P., Fan, L., Phillips, G., Shenker, S.: Web caching and Zipf-like distributions: Evidence and implications. In: Proceedings of INFOCOM 1999, Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies (1999)Google Scholar
  2. 2.
    Gallego, M., Fernández, J., Martínez-Prieto, M., Fuente, P.: An empirical study of real-world SPARQL queries. In: USEWOD 2011 - 1st International Workshop on Usage Analysis and the Web of Data (2011)Google Scholar
  3. 3.
    Harth, A., Decker, S.: Optimized index structures for querying RDF from the web. In: Proceedings of the 3rd Latin American Web Congress (2005)Google Scholar
  4. 4.
    Weiss, C., Karras, P., Bernstein, A.: Hexastore: sextuple indexing for semantic web data management. In: Proceedings of the VLDB Endowment Archive (2008)Google Scholar
  5. 5.
    Neumann, T., Weikum, G.: RDF-3X: a RISC-style engine for rdf. In: Proceedings of the VLDB Endowment Archive (2008)Google Scholar
  6. 6.
    Harris, S., Lamb, N., Shadbolt, N.: 4store: The design and implementation of a clustered rdf store. In: Proceedings of the 5th International Workshop on Scalable Semantic Web Knowledge Base Systems, SSWS 2009 (2009)Google Scholar
  7. 7.
    Goldstein, J., Larson, P.: Optimizing queries using materialized views: a practical, scalable solution. In: Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data (2001)Google Scholar
  8. 8.
    Amiri, K., Park, S., Tewari, R., Padmanabhan, S.: Dbproxy: A dynamic data cache for web applications. In: Proceedings of the 19th International Conference on Data Engineering, ICDE 2003 (2003)Google Scholar
  9. 9.
    Larson, P., Goldstein, J., Zhou, J.: Mtcache: transparent mid-tier database caching in sql server. In: Proceedings of 20th International Conference on Data Engineering, pp. 177–188 (2004)Google Scholar
  10. 10.
    Martin, M., Unbehauen, J., Auer, S.: Improving the Performance of Semantic web Applications with SPARQL Query Caching. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010. LNCS, vol. 6089, pp. 304–318. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Hartig, O.: How caching improves efficiency and result completeness for querying linked data. In: Proceedings of the 4th Linked Data on the Web (LDOW) Workshop (March 2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gregory Todd Williams
    • 1
  • Jesse Weaver
    • 1
  1. 1.Tetherless World ConstellationRensselaer Polytechnic InstituteTroyUSA

Personalised recommendations