Filtering Spurious Events from Event Streams of Business Processes

  • Sebastiaan J. van ZelstEmail author
  • Mohammadreza Fani Sani
  • Alireza Ostovar
  • Raffaele Conforti
  • Marcello La Rosa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10816)


Process mining aims at gaining insights into business processes by analysing event data recorded during process execution. The majority of existing process mining techniques works offline, i.e. using static, historical data stored in event logs. Recently, the notion of online process mining has emerged, whereby techniques are applied on live event streams, as process executions unfold. Analysing event streams allows us to gain instant insights into business processes. However, current techniques assume the input stream to be completely free of noise and other anomalous behaviours. Hence, applying these techniques to real data leads to results of inferior quality. In this paper, we propose an event processor that enables us to filter out spurious events from a live event stream. Our experiments show that we are able to effectively filter out spurious events from the input stream and, as such, enhance online process mining results.


Process mining Event stream Filtering Anomaly detection 



This research is funded by the Australian Research Council (grant DP150103356), and the DELIBIDA research program supported by NWO.


  1. 1.
    van der Aalst, W.M.P.: Process Mining - Data Science in Action. Springer, Heidelberg (2016). Scholar
  2. 2.
    van der Aalst, W.M.P., Bolt, A., van Zelst, S.J.: RapidProM: Mine Your Processes and Not Just Your Data. CoRR abs/1703.03740 (2017)Google Scholar
  3. 3.
    Aggarwal, C.C.: On biased reservoir sampling in the presence of stream evolution. In: Proceedings of the VLDB 2006, pp. 607–618. VLDB Endowment (2006)Google Scholar
  4. 4.
    Babcock, B., Datar, M., Motwani, R.: Sampling from a moving window over streaming data. In: Proceedings of the ACM SODA 2002, pp. 633–634. SIAM (2002)Google Scholar
  5. 5.
    Bifet, A., Gavaldà, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the SDM 2007, pp. 443–448. SIAM (2007)CrossRefGoogle Scholar
  6. 6.
    Burattin, A., Cimitile, M., Maggi, F.M., Sperduti, A.: Online discovery of declarative process models from event streams. IEEE TSC 8(6), 833–846 (2015)Google Scholar
  7. 7.
    Burattin, A., Sperduti, A., van der Aalst, W.M.P.: Control-flow discovery from event streams. In: Proceedings of the CEC 2014, pp. 2420–2427. IEEE (2014)Google Scholar
  8. 8.
    Burattin, A., Carmona, J.: A framework for online conformance checking. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 165–177. Springer, Cham (2018). Scholar
  9. 9.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection for discrete sequences: a survey. IEEE Trans. Knowl. Data Eng. 24(5), 823–839 (2012)CrossRefGoogle Scholar
  10. 10.
    Conforti, R., La Rosa, M., ter Hofstede, A.H.M.: Filtering out infrequent behavior from business process event logs. IEEE TKDE 29(2), 300–314 (2017)Google Scholar
  11. 11.
    Cormode, G., Shkapenyuk, V., Srivastava, D., Xu, B.: Forward decay: a practical time decay model for streaming systems. In: Proceedings of the ICDE 2009, pp. 138–149. IEEE (2009)Google Scholar
  12. 12.
    Dumas, M., La Rosa, M., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management. Springer, Heidelberg (2013). Scholar
  13. 13.
    Fani Sani, M., van Zelst, S.J., van der Aalst, W.M.P.: Improving process discovery results by filtering outliers using conditional behavioural probabilities. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 216–229. Springer, Cham (2018). Scholar
  14. 14.
    Gupta, M., Gao, J., Aggarwal, C.C., Han, J.: Outlier detection for temporal data: a survey. IEEE Trans. Knowl. Data Eng. 26(9), 2250–2267 (2014)CrossRefGoogle Scholar
  15. 15.
    Hassani, M., Siccha, S., Richter, F., Seidl, T.: Efficient process discovery from event streams using sequential pattern mining. In: Proceedings of the SSCI 2015, pp. 1366–1373. IEEE (2015)Google Scholar
  16. 16.
    Maaradji, A., Dumas, M., La Rosa, M., Ostovar, A.: Detecting sudden and gradual drifts in business processes from execution traces. IEEE TKDE 29(10), 2140–2154 (2017)Google Scholar
  17. 17.
    Mannhardt, F.: Sepsis Cases - Event Log. Eindhoven University of Technology (2016).
  18. 18.
    Marquez-Chamorro, A., Resinas, M., Ruiz-Cortes, A.: Predictive monitoring of business processes: a survey. IEEE Trans. Serv. Comput. (2017).
  19. 19.
    Ostovar, A., Maaradji, A., La Rosa, M., ter Hofstede, A.H.M.: Characterizing drift from event streams of business processes. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 210–228. Springer, Cham (2017). Scholar
  20. 20.
    Ostovar, A., Maaradji, A., La Rosa, M., ter Hofstede, A.H.M., van Dongen, B.F.V.: Detecting drift from event streams of unpredictable business processes. In: Comyn-Wattiau, I., Tanaka, K., Song, I.-Y., Yamamoto, S., Saeki, M. (eds.) ER 2016. LNCS, vol. 9974, pp. 330–346. Springer, Cham (2016). Scholar
  21. 21.
    Verbeek, H.M.W., Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: XES, XESame, and ProM 6. In: Soffer, P., Proper, E. (eds.) CAiSE Forum 2010. LNBIP, vol. 72, pp. 60–75. Springer, Heidelberg (2011). Scholar
  22. 22.
    Vitter, J.S.: Random sampling with a reservoir. ACM TOMS 11(1), 37–57 (1985)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Wang, J., Song, S., Lin, X., Zhu, X., Pei, J.: Cleaning Structured event logs: a graph repair approach. In: Proceedings of the ICDE 2015, pp. 30–41. IEEE (2015)Google Scholar
  24. 24.
    Weber, B., Reichert, M., Rinderle-Ma, S.: Change patterns and change support features - enhancing flexibility in process-aware information systems. DKE 66(3), 438–466 (2008)CrossRefGoogle Scholar
  25. 25.
    van Zelst, S.J., van Dongen, B.F., van der Aalst, W.M.P.: Event stream-based process discovery using abstract representations. KAIS 54, 407–435 (2017)Google Scholar
  26. 26.
    van Zelst, S.J., Bolt, A., Hassani, M., van Dongen, B.F., van der Aalst, W.M.P.: Online Conformance Checking: Relating Event Streams to Process Models using Prefix-Alignments. IJDSA (2017).

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Sebastiaan J. van Zelst
    • 1
    Email author
  • Mohammadreza Fani Sani
    • 2
  • Alireza Ostovar
    • 3
  • Raffaele Conforti
    • 4
  • Marcello La Rosa
    • 4
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.RWTH Aachen UniversityAachenGermany
  3. 3.Queensland University of TechnologyBrisbaneAustralia
  4. 4.University of MelbourneMelbourneAustralia

Personalised recommendations