Continuously Self-Updating Query Results over Dynamic Heterogeneous Linked Data

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9678)

Abstract

Our society is evolving towards massive data consumption from heterogeneous sources, which includes rapidly changing data like public transit delay information. Many applications that depend on dynamic data consumption require highly available server interfaces. Existing interfaces involve substantial costs to publish rapidly changing data with high availability, and are therefore only possible for organisations that can afford such an expensive infrastructure. In my doctoral research, I investigate how to publish and consume real-time and historical Linked Data on a large scale. To reduce server-side costs for making dynamic data publication affordable, I will examine different possibilities to divide query evaluation between servers and clients. This paper discusses the methods I aim to follow together with preliminary results and the steps required to use this solution. An initial prototype achieves significantly lower server processing cost per query, while maintaining reasonable query execution times and client costs. Given these promising results, I feel confident this research direction is a viable solution for offering low-cost dynamic Linked Data interfaces as opposed to the existing high-cost solutions.

Keywords

Linked Data Triple Pattern Fragments sparql Continuous querying Real-time querying 

References

  1. 1.
    Ali, M.I., Gao, F., Mileo, A.: CityBench: a configurable benchmark to evaluate RSP engines using smart city datasets. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9367, pp. 374–389. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25010-6_25 CrossRefGoogle Scholar
  2. 2.
    Arasu, A., Babcock, B., Babu, S., Cieslewicz, J., Datar, M., Ito, K., Motwani, R., Srivastava, U., Widom, J.: stream: the Stanford data stream management system. Book chapter (2004). http://ilpubs.stanford.edu:8090/641/1/2004-20.pdf
  3. 3.
    Babu, S., Widom, J.: Continuous queries over data streams. ACM Sigmod Rec. 30(3), 109–120 (2001). http://dl.acm.org/citation.cfm?id=603884 CrossRefGoogle Scholar
  4. 4.
    Barbieri, D., Braga, D., Ceri, S., Della Valle, E., Grossniklaus, M.: Stream reasoning: where we got so far. In: Proceedings of the NeFoRS2010 Workshop, Co-located with ESWC 2010 (2010). http://wasp.cs.vu.nl/larkc/nefors10/paper/nefors10_paper_0.pdf
  5. 5.
    Barbieri, D.F., Braga, D., Ceri, S., Valle, E.D., Grossniklaus, M.: Querying rdf streams with c-sparql. SIGMOD Rec. 39(1), 20–26 (2010)CrossRefMATHGoogle Scholar
  6. 6.
    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, Part II. LNCS, vol. 8219, pp. 277–293. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    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). http://www.few.vu.nl/ frankh/postscript/IEEE-IS09.pdf CrossRefGoogle Scholar
  8. 8.
    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). http://www.sciencedirect.com/science/article/pii/S1570826813000036 CrossRefGoogle Scholar
  9. 9.
    Fisteus, J.A., Garcia, N.F., Fernandez, L.S., Fuentes-Lorenzo, D.: Ztreamy: a middleware for publishing semantic streams on the web. Web Semant. Sci. Serv. Agents World Wide Web 25, 16–23 (2014)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., et al. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 370–388. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Meinhardt, P., Knuth, M., Sack, H.: TailR: a platform for preserving history on the web of data. In: Proceedings of the 11th International Conference on Semantic Systems, pp. 57–64. ACM (2015). http://dl.acm.org/citation.cfm?id=2814875
  12. 12.
    Pérez, J., Arenas, M., Gutierrez, C.: Semantics and complexity of sparql. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 30–43. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Sheth, A., Henson, C., Sahoo, S.: Semantic sensor web. IEEE Internet Comput. 12(4), 78–83 (2008). http://corescholar.libraries.wright.edu/cgi/viewcontent.cgi?article=2125&context=knoesis
  14. 14.
    de Sompel, H.V., Nelson, M.L., Sanderson, R., Balakireva, L., Ainsworth, S., Shankar, H.: Memento: time travel for the web. CoRR abs/0911.1112 (2009). http://arxiv.org/abs/0911.1112
  15. 15.
    de Sompel, H.V., Sanderson, R., Nelson, M.L., Balakireva, L., Shankar, H., Ainsworth, S.: An http-based versioning mechanism for linked data. CoRR abs/1003.3661 (2010). http://arxiv.org/abs/1003.3661
  16. 16.
    Taelman, R.: Continuously updating queries over real-time linked data. Master’s thesis, Ghent University, Belgium (2015). http://rubensworks.net/raw/publications/2015/continuously_updating_queries_over_real-time_linked_data.pdf
  17. 17.
    Vander Sande, M., Colpaert, P., Verborgh, R., Coppens, S., Mannens, E., Van de Walle, R.: R&Wbase: git for triples. In: LDOW (2013). http://events.linkeddata.org/ldow2013/papers/ldow2013-paper-01.pdf
  18. 18.
    Verborgh, R., Hartig, O., De Meester, B., Haesendonck, G., De Vocht, L., Vander Sande, M., Cyganiak, R., Colpaert, P., Mannens, E., Van de Walle, R.: Querying datasets on the Web with high availability. In: Proceedings of the 13th International Semantic Web Conference (2014). http://linkeddatafragments.org/publications/iswc2014.pdf
  19. 19.
    Verborgh, R., Vander Sande, M., Colpaert, P., Coppens, S., Mannens, E., Van de Walle, R.: Web-scale querying through Linked Data Fragments. In: Proceedings of the 7th Workshop on Linked Data on the Web (2014). http://events.linkeddata.org/ldow2014/papers/ldow2014_paper_04.pdf
  20. 20.
    Zhang, Y., Duc, P.M., Corcho, O., Calbimonte, J.-P.: SRBench: a streaming rdf/sparql benchmark. In: Heflin, J., et al. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 641–657. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Data Science Lab (Ghent University - iMinds)GhentBelgium

Personalised recommendations