Managing Uncertain Complex Events in Web of Things Applications

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10845)


A critical issue in the Web of Things (WoT) is the need to process and analyze the interactions of Web-interconnected real-world objects. Complex Event Processing (CEP) is a powerful technology for analyzing streams of information about real-time distributed events, coming from different sources, and for extracting conclusions from them. However, in many situations these events are not free from uncertainty, due to either unreliable data sources and networks, measurement uncertainty, or to the inability to determine whether an event has actually happened or not. This short research paper discusses how CEP systems can incorporate different kinds of uncertainty, both in the events and in the rules. A case study is used to validate the proposal, and we discuss the benefits and limitations of this CEP extension.



This work was partially supported by the Spanish Government under Grant TIN2014-52034-R. We would like to thank the reviewers of this paper for their valuable comments and suggestions.


  1. 1.
    Alevizos, E., Skarlatidis, A., Artikis, A., Paliouras, G.: Complex event processing under uncertainty: a short survey. In: Proceedings of the Workshops of the EDBT/ICDT 2015 Joint Conference. vol. 1330 of CEUR Workshop Proceedings,, pp. 97–103 (2015)Google Scholar
  2. 2.
    Bertoa, M.F., Moreno, N., Barquero, G., Burgueño, L., Troya, J., Vallecillo, A.: Expressing uncertainty in OCL/UML datatypes. In: Technical report (2018). Submitted
  3. 3.
    Cugola, G., Margara, A.: Processing flows of information: from data stream to complex event processing. ACM Comput. Surv. 44(3), 15:1–15:62 (2012)CrossRefGoogle Scholar
  4. 4.
    Cugola, G., Margara, A., Matteucci, M., Tamburrelli, G.: Introducing uncertainty in complex event processing: model, implementation, and validation. Computing 97(2), 103–144 (2015)CrossRefGoogle Scholar
  5. 5.
    Cugola, G., Margara, A., Pezzè, M., Pradella, M.: Efficient analysis of event processing applications. In: Proceedings of DEBS 2015, pp. 10–21. ACM (2015)Google Scholar
  6. 6.
    EsperTech: Esper - Complex Event Processing. Accessed 18 Nov 2017
  7. 7.
    Etzion, O., Niblett, P.: Event Processing in Action. Manning Publications, Stamford (2010)Google Scholar
  8. 8.
    Garcia-de Prado, A., Ortiz, G., Boubeta-Puig, J.: COLLECT: COLLaborativE ConText-aware service oriented architecture for intelligent decision-making in the Internet of Things. Expert Syst. Appl. 85, 231–248 (2017)CrossRefGoogle Scholar
  9. 9.
    Greengard, S.: The Internet of Things. MIT Press, Cambridge (2015)Google Scholar
  10. 10.
    JCGM 100:2008: Evaluation of measurement data–Guide to the expression of uncertainty in measurement (GUM). Joint Com. for Guides in Metrology (2008).
  11. 11.
    Kawashima, H., Kitagawa, H., Li, X.: Complex event processing over uncertain data streams. In: Proceedings of 3PGCIC 2010, pp. 521–526. IEEE Computer Society (2010)Google Scholar
  12. 12.
    Luckham, D.C.: The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Addison-Wesley, Boston (2002)Google Scholar
  13. 13.
    Luckham, D.C.: Event Processing for Business: Organizing the Real-Time Enterprise. Wiley, Hoboken (2012)CrossRefGoogle Scholar
  14. 14.
    Mayerhofer, T., Wimmer, M., Vallecillo, A.: Adding uncertainty and units to quantity types in software models. In: Proceedings of SLE 2016, pp. 118–131 (2016)Google Scholar
  15. 15.
    Wang, Y.H., Cao, K., Zhang, X.M.: Complex event processing over distributed probabilistic event streams. Comput. Math. Appl. 66(10), 1808–1821 (2013)CrossRefGoogle Scholar
  16. 16.
    Wasserkrug, S., Gal, A., Etzion, O., Turchin, Y.: Complex event processing over uncertain data. In: Proceedings of DEBS 2008, pp. 253–264. ACM (2008)Google Scholar
  17. 17.
    Xu, C., Lin, S., Lei, W., Qiao, J.: Complex event detection in probabilistic stream. In: Proceedings of APWEB 2010, pp. 361–363. IEEE Computer Society (2010)Google Scholar
  18. 18.
    Zhang, H., Diao, Y., Immerman, N.: Recognizing patterns in streams with imprecise timestamps. Inf. Syst. 38(8), 1187–1211 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Universidad de MálagaMálagaSpain
  2. 2.Universidad de SevillaSevillaSpain

Personalised recommendations