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Optimized Processing of Subscriptions to DBpedia Live

  • Kia Teymourian
  • Alexandru Todor
  • Wojciech Łukasiewicz
  • Adrian Paschke
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 228)

Abstract

DBpedia Live enables access to structured data extracted from Wikipedia in real-time. A data stream that is generated from Wikipedia changes is instantly loaded in the DBpedia RDF store. Applications can benefit by subscribing to the RDF update stream and receive continuous results from DBpedia. Providing a continuous update stream of changes to subscribed DBpedia queries is a challenging task due to the load it places on the RDF store.

In this paper, we propose an optimization approach for processing subscriptions to DBpedia Live. By monitoring the change data stream, query processing can be optimized to avoid unnecessary processing load by continuous database polling. Queries are only re-processed when the system can detect a relation between incoming changes and queries so that it can trigger the processing of the specific query. We evaluated our approach by using a recorded history of the DBpedia change stream and as queries we used the most frequent DBpedia SPARQL queries obtained from the logs. A comparison of our approach to the interval-based database polling approach shows a significant optimization of processing costs.

Keywords

Query Processing User Query SPARQL Query Triple Pattern Link Open 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

This work has been partially supported by the “InnoProfile-Transfer Corporate Smart Content” project funded by the German Federal Ministry of Education and Research (BMBF) and the BMBF Innovation Initiative for the New German Länder-Entrepreneurial Regions.

References

  1. 1.
    Anicic, D., Fodor, P., Rudolph, S., Stojanovic, N.: Ep-sparql: a unified language for event processing and stream reasoning. In: Proceedings of the 20th International Conference on World Wide Web, WWW 2011, pp. 635–644. ACM, New York (2011)Google Scholar
  2. 2.
    Anicic, D., Fodor, P., Rudolph, S., Stühmer, R., Stojanovic, N., Studer, R.: ETALIS: rule-based reasoning in event processing. In: Helmer, S., Poulovassilis, A., Xhafa, F. (eds.) Reasoning in Event-Based Distributed Systems. SCI, vol. 347, pp. 99–124. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  3. 3.
    Arias, M., Fernández, J.D., Martínez-Prieto, M.A., de la Fuente, P.: An empirical study of real-world sparql queries. arXiv preprint arXiv:1103.5043 (2011)
  4. 4.
    Barbieri, D.F., Braga, D., Ceri, S., Grossniklaus, M.: An execution environment for c-sparql queries. In: Proceedings of the 13th International Conference on Extending Database Technology, EDBT 2010, pp. 441–452. ACM, New York (2010)Google Scholar
  5. 5.
    Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: Dbpedia - a crystallization point for the web of data. Web Semant. 7(3), 154–165 (2009)CrossRefGoogle Scholar
  6. 6.
    Bolles, A., Grawunder, M., Jacobi, J.: Streaming SPARQL - extending SPARQL to process data streams. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 448–462. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  7. 7.
    Calbimonte, J.-P., Corcho, O., Gray, A.J.G.: Enabling ontology-based access to streaming data sources. 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. 96–111. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  8. 8.
    Danh, L.P., Minh, D.T., Minh Duc, P., Boncz, P.A., Thomas, E., Michael, F.: Linked stream data processing: facts and figures, 01 November 2012Google Scholar
  9. 9.
    Eugster, P.T., Felber, P.A., Guerraoui, R., Kermarrec, A.-M.: The many faces of publish/subscribe. ACM Comput. Surv. 35(2), 114–131 (2003)CrossRefGoogle Scholar
  10. 10.
    Le-Phuoc, D., Dao-Tran, M., Xavier Parreira, J., Hauswirth, M.: A native and adaptive approach for unified processing of linked streams and 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. 370–388. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  11. 11.
    Liu, Y., Plale, B.: Survey of publish subscribe event systems. Technical report, Indiana University (2003)Google Scholar
  12. 12.
    Passant, A., Mendes, P.N.: sparqlpush: Proactive notification of data updates in rdf stores using pubsubhubbub. In: Proceedings of the 6th Workshop on Scripting and Development for the Semantic Web (SFSW2010) co-located with ESWC 2010 (2010)Google Scholar
  13. 13.
    Sequeda, J., Corcho, Ó.: Linked stream data: a position paper, pp. 148–157 (2009)Google Scholar
  14. 14.
    Della Valle, E., Ceri, S., van Harmelen, F., Fensel, D.: It’s a streaming world! reasoning upon rapidly changing information. IEEE Intell. Syst. 24(6), 83–89 (2009)CrossRefGoogle Scholar
  15. 15.
    Zhang, Y., Duc, P.M., Groffen, F., Liarou, E., Boncz, P., Kersten, M., Calbimonte, J.-P., Corcho, O.: Benchmarking RDF storage engines. Deliverable D1.2. Technical report, PlanetData FP7 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Kia Teymourian
    • 1
  • Alexandru Todor
    • 1
  • Wojciech Łukasiewicz
    • 1
  • Adrian Paschke
    • 1
  1. 1.Institute for Computer Science, AG Corporate Semantic WebFreie Universität BerlinBerlinGermany

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