A Query Model to Capture Event Pattern Matching in RDF Stream Processing Query Languages

  • Daniele Dell’Aglio
  • Minh Dao-Tran
  • Jean-Paul Calbimonte
  • Danh Le Phuoc
  • Emanuele Della Valle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10024)

Abstract

The current state of the art in RDF Stream Processing (RSP) proposes several models and implementations to combine Semantic Web technologies with Data Stream Management System (DSMS) operators like windows. Meanwhile, only a few solutions combine Semantic Web and Complex Event Processing (CEP), which includes relevant features, such as identifying sequences of events in streams. Current RSP query languages that support CEP features have several limitations: EP-SPARQL can identify sequences, but its selection and consumption policies are not all formally defined, while C-SPARQL offers only a naive support to pattern detection through a timestamp function. In this work, we introduce an RSP query language, called RSEP-QL, which supports both DSMS and CEP operators, with a special interest in formalizing CEP selection and consumption policies. We show that RSEP-QL captures EP-SPARQL and C-SPARQL, and offers features going beyond the ones provided by current RSP query languages.

References

  1. 1.
    Agrawal, J., Diao, Y., Gyllstrom, D., Immerman, N.: Efficient pattern matching over event streams. In: SIGMOD, pp. 147–160 (2008)Google Scholar
  2. 2.
    Anicic, D.: Event processing and stream reasoning with ETALIS. Ph.D. thesis, Karlsruhe Institute of Technology (2011)Google Scholar
  3. 3.
    Anicic, D., Fodor, P., Rudolph, S., Stojanovic, N.: EP-SPARQL: a unified language for event processing and stream reasoning. In: WWW, pp. 635–644 (2011)Google Scholar
  4. 4.
    Arasu, A., Babu, S., Widom, J.: The CQL continuous query language: semantic foundations and query execution. VLDB J. 15(2), 121–142 (2006)CrossRefGoogle Scholar
  5. 5.
    Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and issues in data stream systems. In: PODS, pp. 1–16. ACM (2002)Google Scholar
  6. 6.
    Barbieri, D.F., Braga, D., Ceri, S., Della Valle, E., Grossniklaus, M.: C-SPARQL: a continuous query language for RDF data streams. Int. J. Semant. Comput. 4(1), 3–25 (2010)CrossRefMATHGoogle Scholar
  7. 7.
    Beck, H., Dao-Tran, M., Eiter, T., Fink, M.: LARS: a logic-based framework for analyzing reasoning over streams. In: AAAI, pp. 1431–1438 (2015)Google Scholar
  8. 8.
    Botan, I., Derakhshan, R., Dindar, N., Haas, L.M., Miller, R.J., Tatbul, N.: SECRET: a model for analysis of the execution semantics of stream processing systems. PVLDB 3(1), 232–243 (2010)Google Scholar
  9. 9.
    Calbimonte, J.P., Jeung, H., Corcho, Ó., Aberer, K.: Enabling query technologies for the semantic sensor web. Int. J. Semant. Web Inf. Syst. 8(1), 43–63 (2012)CrossRefGoogle Scholar
  10. 10.
    Cugola, G., Margara, A.: Processing flows of information: from data stream to complex event processing. ACM Comput. Surv. 44(3), 15:1–15:62 (2011)Google Scholar
  11. 11.
    Dao-Tran, M., Beck, H., Eiter, T.: Contrasting RDF stream processing semantics. In: Qi, G., Kozaki, K., Pan, J.Z., Yu, S. (eds.) JIST 2015. LNCS, vol. 9544, pp. 289–298. Springer, Heidelberg (2016). doi:10.1007/978-3-319-31676-5_21 CrossRefGoogle Scholar
  12. 12.
    Dao-Tran, M., Le-Phuoc, D.: Towards enriching CQELS with complex event processing and path navigation. In: HiDeSt, pp. 2–14 (2015)Google Scholar
  13. 13.
    Dell’Aglio, D., Balduini, M., Della Valle, E.: On the need to include functional testing in RDF stream engine benchmarks. In: 10th ESWC 2013 Conference Workshops: BeRSys 2013, AImWD 2013 and USEWOD 2013 (2013)Google Scholar
  14. 14.
    Dell’Aglio, D., Valle, E.D., Calbimonte, J., Corcho, O.: RSP-QL semantics: a unifying query model to explain heterogeneity of RDF stream processing systems. Int. J. Semant. Web Inf. Syst. 10(4), 17–44 (2014)CrossRefGoogle Scholar
  15. 15.
    Gutierrez, C., Hurtado, C., Vaisman, A.: Introducing time into RDF. IEEE Trans. Knowl. Data Eng. 19(2), 207–218 (2007)CrossRefGoogle Scholar
  16. 16.
    Harris, S., Seaborne, A.: SPARQL 1.1 Query Language (2013). http://www.w3.org/TR/sparql11-query/
  17. 17.
    Komazec, S., Cerri, D., Fensel, D.: Sparkwave: continuous schema-enhanced pattern matching over RDF data streams. In: DEBS, pp. 58–68 (2012)Google Scholar
  18. 18.
    Luckham, D.C.: The power of events - an introduction to complex event processing in distributed enterprise systems. ACM (2005)Google Scholar
  19. 19.
    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
  20. 20.
    Rinne, M., Törmä, S., Nuutila, E.: SPARQL-based applications for RDF-encoded sensor data. In: SSN, vol. 904, pp. 81–96 (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Daniele Dell’Aglio
    • 1
    • 2
  • Minh Dao-Tran
    • 3
  • Jean-Paul Calbimonte
    • 4
  • Danh Le Phuoc
    • 5
  • Emanuele Della Valle
    • 2
  1. 1.Department of InformaticsUniversity of ZurichZurichSwitzerland
  2. 2.Dipartimento di Elettronica, Informatica e BioingegneriaPolitecnico of MilanoMilanoItaly
  3. 3.Institute of Information SystemsVienna University of TechnologyViennaAustria
  4. 4.Institute of Information SystemsHES-SO Valais-Wallis and LSIR, EPFLLausanneSwitzerland
  5. 5.Technical University of BerlinBerlinGermany

Personalised recommendations