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

Part of the Lecture Notes in Computer Science book series (LNAI,volume 10024)


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.


  • Window Function
  • Event Mapping
  • Input Stream
  • Graph Pattern
  • Event Pattern

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.

This research has been supported by the Austrian Science Fund (FWF) project P26471, the D1NAMO project, and the Marie Skłodowska-Curie Programme H2020-MSCA-IF-2014 under Grant No. 661180.

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  1. Agrawal, J., Diao, Y., Gyllstrom, D., Immerman, N.: Efficient pattern matching over event streams. In: SIGMOD, pp. 147–160 (2008)

    Google Scholar 

  2. Anicic, D.: Event processing and stream reasoning with ETALIS. Ph.D. thesis, Karlsruhe Institute of Technology (2011)

    Google Scholar 

  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. Arasu, A., Babu, S., Widom, J.: The CQL continuous query language: semantic foundations and query execution. VLDB J. 15(2), 121–142 (2006)

    CrossRef  Google Scholar 

  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. 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)

    CrossRef  MATH  Google Scholar 

  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. 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. 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)

    CrossRef  Google Scholar 

  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. 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

    CrossRef  Google Scholar 

  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. 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. 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)

    CrossRef  Google Scholar 

  15. Gutierrez, C., Hurtado, C., Vaisman, A.: Introducing time into RDF. IEEE Trans. Knowl. Data Eng. 19(2), 207–218 (2007)

    CrossRef  Google Scholar 

  16. Harris, S., Seaborne, A.: SPARQL 1.1 Query Language (2013).

  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. Luckham, D.C.: The power of events - an introduction to complex event processing in distributed enterprise systems. ACM (2005)

    Google Scholar 

  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)

    CrossRef  Google Scholar 

  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 

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Correspondence to Daniele Dell’Aglio .

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Dell’Aglio, D., Dao-Tran, M., Calbimonte, JP., Le Phuoc, D., Della Valle, E. (2016). A Query Model to Capture Event Pattern Matching in RDF Stream Processing Query Languages. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds) Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science(), vol 10024. Springer, Cham.

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