, Volume 15, Issue 2, pp 119–129 | Cite as

Complex Event Processing on Linked Stream Data

  • Omran Saleh
  • Stefan Hagedorn
  • Kai-Uwe Sattler


Social networks and Sensor Web technologies typically generate a massive amount of data published as streams. In order to give these streams a meaningful sense and enrich them with semantic descriptions, the concept of Linked Stream Data (LSD) has emerged. However, to support a wide range of LSD scenarios and queries comprehensive solutions providing not only classic data stream operators such as windows, but also for processing of complex events, linking of (static) datasets, and scalable processing are required. In this paper, we present our approach for processing LSD and addressing these requirements. In contrast to existing LSD engines relying on streaming extensions to SPARQL, our PipeFlow system is a (relational) dataflow language and engine providing support for complex event processing (CEP) and a few dedicated operators for RDF data. We describe this language and particularly the CEP model as well as the system architecture for parallel CEP and LSD processing by exploiting partitioning techniques for cluster environments. Finally, we report results from experiments evaluating our system in comparison to existing LSD engines.


Stream Processing Continuous Query Complex Event Processing Kleene Star Pattern Match 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.
    Abadi DJ, Ahmad Y, Balazinska M, Cherniack M, hyon Hwang J, Lindner W, Maskey AS, Rasin E. Ryvkina E, Tatbul N, Xing Y, Zdonik S (2005) The design of the borealis stream processing engine. CIDR Conference, Asilomar, pp 277–289Google Scholar
  2. 2.
    Abadi DJ, Carney D, Cetintemel U, Cherniack M, Convey C, Lee S, Stonebraker M, Tatbul N, Zdonik S (2003) Aurora: a new model and architecture for data stream management. VLDB J 12(2):120–139Google Scholar
  3. 3.
    Agrawal J, Diao Y, Gyllstrom D, Immerman N (2008) Efficient pattern matching over event streams. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, SIGMOD '08. ACM, New York, pp 147-160Google Scholar
  4. 4.
    Anicic D, Fodor P, Rudolph S, Stojanovic N (2011) EP-SPARQL: a unified language for event processing and stream reasoning. In: Proceedings of the 20th International Conference on World Wide Web, WWW '11. ACM, New York, pp 635-633Google Scholar
  5. 5.
    Barbieri DF, Braga D, Ceri S, Della Valle E, Grossniklaus M (2009) C-SPARQL: SPARQL for continuous querying. In: Proceedings of the 18th International Conference on World Wide Web, WWW '09. ACM, New York, pp 1061–1062Google Scholar
  6. 6.
    Barbieri DF, Braga D, Ceri S, Valle ED, Grossniklaus M (2010) Querying RDF streams with C-SPARQL. SIGMOD Rec 39(1):20–26Google Scholar
  7. 7.
    Beckett D Redland. Accessed 15 Feb 2015
  8. 8.
    Bolles A, Grawunder M, Jacobi J (2008) Streaming SPARQL extending SPARQL to process data streams. In: Proceedings of the 5th European Semantic Web Conference on The Semantic Web: Research and Applications, ESWC'08. Springer, Berlin, pp 448–462Google Scholar
  9. 9.
    Chakravarthy S, Mishra D (1994) Snoop: an expressive event specification language for active databases. Data Knowl Eng 14(1):1–26Google Scholar
  10. 10.
    Dean J, Ghemawat S (2004) Mapreduce: Simplified data processing on large clusters. In: OSDI. ACM, New York, pp 137-150Google Scholar
  11. 11.
    Demers A, Gehrke J, Panda B, Riedewald M, Sharma V, White W (2007) Cayuga: a general purpose event monitoring system. In: Conference on Innovative Data Systems Research. ACM, New York, pp 412-422Google Scholar
  12. 12.
    Digital Marketing Ramblings Amazing facebook user statictics. Accessed 15 Feb 2015
  13. 13.
    Distributed Stream Computing Platform: S4. Accessed 15 Feb 2015
  14. 14.
    Gedik B, Schneider S, Hirzel M, Wu K (2014) Elastic scaling for data stream processing. IEEE Trans Parallel Distrib Syst 25(6):1447–1463Google Scholar
  15. 15.
    Le-Phuoc D, Dao-Tran M, Parreira JX, Hauswirth M (2011) A native and adaptive approach for unified processing of linked streams and linked data. In: Proceedings of the 10th ISWC—Volume Part I, ISWC'11. Springer, Berlin, pp. 370–388Google Scholar
  16. 16.
    Le-Phuoc D, Nguyen Mau Quoc H, Le Van C, Hauswirth M (2013) Elastic and scalable processing of linked stream data in the cloud. In: The Semantic Web—ISWC 2013, Lecture Notes in Computer Science, vol. 8218. Springer, Berlin, pp. 280–297Google Scholar
  17. 17.
    Margara A, Urbani J, van Harmelen F, Bal H (2014) Streaming the web: reasoning over dynamic data. Web Semantics: Science, Services and Agents on the World Wide Web 25(0)Google Scholar
  18. 18.
    Olston C, Reed B, Srivastava U, Kumar R, Tomkins A (2008) Pig latin: a not-so-foreign language for data processing. In: SIGMOD. ACM, New YorkGoogle Scholar
  19. 19.
    Phuoc DL, Dao-Tran M, Pham MD, Boncz PA, Eiter T, Fink M (2012) Linked stream data processing engines: facts and figures. In: International Semantic Web Conference (2), Lecture Notes in Computer Science, vol. 7650. Springer, pp. 300–312Google Scholar
  20. 20.
    Saleh O, Betz H, Sattler K (2015) Partitioning for scalable complex event processing on data streams. In: New Trends in Database and Information Systems II, Advances in Intelligent Systems and Computing, vol. 312. Springer International Publishing, Cham, pp. 185–197Google Scholar
  21. 21.
    Saleh O, Sattler K (2014) On efficient processing of linked stream data. In: On the Move to Meaningful Internet Systems: OTM 2014 Conferences, LNCS, vol. 8841. Springer, Berlin, pp. 700–717Google Scholar
  22. 22.
    Spark A Spark: lightning-fast cluster computing. Accessed 15 Feb 2015
  23. 23.
    Sriskandarajah S, Kasun G, Isuru LN, Subash C, Srinath P, Vishaka N (2011) Siddhi: a second look at complex event processing architectures. In: Proceedings of the 2011 ACM workshop on Gateway computing environments, GCE '11. ACM, New York, pp. 43–50Google Scholar
  24. 24.
    {Stanford University}: Stanford Stream Data Manager. Accessed 15 Feb 2015
  25. 25.
    Tucker PA, Maier D, Sheard T, Fegaras L (2003) Exploiting punctuation semantics in continuous data streams. IEEE Trans Knowl Data Eng 15(3):555–568Google Scholar
  26. 26.
    Twitter: Storm, distributed and fault-tolerant realtime computation. Accessed 15 Feb 2015

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Computer Science and Automation, Database and Information Systems Group, Technische Universität IlmenauIlmenauGermany

Personalised recommendations