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Process Histories - Detecting and Representing Concept Drifts Based on Event Streams

Part of the Lecture Notes in Computer Science book series (LNPSE,volume 11229)


Business processes have to constantly adapt in order to react to changes induced by, e.g., new regulations or customer needs resulting in so called concept drifts. By now techniques to detect concept drifts are applied on process execution logs ex post, i.e., after the process is finished. However, detecting concept drifts during run-time bears many benefits such as instant reaction to the concept drift. Introducing process histories as a novel way to detect and represent incremental, sudden, recurring, and gradual concept drifts through mining the evolution of a process model based on an event stream will face this challenge. Therefore, a formal definition of process histories is given, the concept of process histories is prototypically implemented and compared with existing approaches based on a synthetic event log.


  • Process mining
  • Event streams
  • Runtime
  • Concept drift
  • Process histories

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  • DOI: 10.1007/978-3-030-02610-3_18
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  1. 1.

    Extensible Event Stream.


  1. IEEE standard for extensible event stream (XES) for achieving interoperability in event logs and event streams. IEEE Std 1849–2016, pp. 1–50 (Nov 2016)

    Google Scholar 

  2. Van der Aalst, W., Adriansyah, A., van Dongen, B.: Replaying history on process models for conformance checking and performance analysis. Wiley Interdiscip. Rev.: Data Min. Knowl. Discov. 2(2), 182–192 (2012)

    Google Scholar 

  3. van der Aalst, W.M., Bolt, A., van Zelst, S.J.: RapidProM: mine your processes and not just your data. arXiv preprint arXiv:1703.03740 (2017)

  4. Bose, R.P.J.C., van der Aalst, W.M.P., Žliobaitė, I., Pechenizkiy, M.: Handling concept drift in process mining. In: Mouratidis, H., Rolland, C. (eds.) CAiSE 2011. LNCS, vol. 6741, pp. 391–405. Springer, Heidelberg (2011).

    CrossRef  Google Scholar 

  5. Bose, R.J.C., Van Der Aalst, W.M., Zliobaite, I., Pechenizkiy, M.: Dealing with concept drifts in process mining. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 154–171 (2014)

    CrossRef  Google Scholar 

  6. Burattin, A., Sperduti, A., van der Aalst, W.M.: Heuristics miners for streaming event data. arXiv preprint arXiv:1212.6383 (2012)

  7. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms. MIT Press (2009)

    Google Scholar 

  8. Drolet, M.: How much will non-compliance with GDPR cost you? CSO, October 2017.

  9. Goodman, R.M., Smyth, P.: Rule induction using information theory. G. Piatetsky (1991)

    Google Scholar 

  10. Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs - a constructive approach. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 311–329. Springer, Heidelberg (2013).

    CrossRef  Google Scholar 

  11. Maggi, F.M., Burattin, A., Cimitile, M., Sperduti, A.: Online process discovery to detect concept drifts in LTL-based declarative process models. In: Meersman, R., et al. (eds.) OTM 2013. LNCS, vol. 8185, pp. 94–111. Springer, Heidelberg (2013).

    CrossRef  Google Scholar 

  12. Matsumoto, Y., Ishituka, K.: Ruby programming language (2002)

    Google Scholar 

  13. Alves de Medeiros, A., Van Dongen, B., Van Der Aalst, W., Weijters, A.: Process mining: extending the alpha-algorithm to mine short loops. Technical report, BETA Working Paper Series (2004)

    Google Scholar 

  14. Peterson, J.L.: Petri net theory and the modeling of systems (1981)

    Google Scholar 

  15. Reichert, M., Weber, B.: Enabling Flexibility in Process-Aware Information Systems: Challenges, Methods, Technologies. Springer, Heidelberg (2012).

    CrossRef  MATH  Google Scholar 

  16. Rinderle, S., Reichert, M., Jurisch, M., Kreher, U.: On representing, purging, and utilizing change logs in process management systems. In: Dustdar, S., Fiadeiro, J.L., Sheth, A.P. (eds.) BPM 2006. LNCS, vol. 4102, pp. 241–256. Springer, Heidelberg (2006).

    CrossRef  Google Scholar 

  17. Rozinat, A., Van der Aalst, W.M.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)

    CrossRef  Google Scholar 

  18. van der Aalst, W., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012).

    CrossRef  Google Scholar 

  19. Van Der Aalst, W., Van Hee, K.M., van Hee, K.: Workflow Management: Models, Methods, and Systems. MIT Press (2004)

    Google Scholar 

  20. Weijters, A., van Der Aalst, W.M., De Medeiros, A.A.: Process mining with the heuristics miner-algorithm. Technische Universiteit Eindhoven, Technical report WP 166, pp. 1–34 (2006)

    Google Scholar 

  21. Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Mach. Learn. 23(1), 69–101 (1996)

    Google Scholar 

  22. van Zelst, S.J., Bolt, A., Hassani, M., van Dongen, B.F., van der Aalst, W.M.: Online conformance checking: relating event streams to process models using prefix-alignments. Int. J. Data Sci. Anal. 1–16 (2017)

    Google Scholar 

  23. van Zelst, S.J., van Dongen, B.F., van der Aalst, W.M.: Event stream-based process discovery using abstract representations. Knowl. Inf. Syst. 54(2), 407–435 (2018)

    CrossRef  Google Scholar 

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This work has been funded by the Vienna Science and Technology Fund (WWTF) through project ICT15-072.

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Correspondence to Florian Stertz .

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Stertz, F., Rinderle-Ma, S. (2018). Process Histories - Detecting and Representing Concept Drifts Based on Event Streams. In: Panetto, H., Debruyne, C., Proper, H., Ardagna, C., Roman, D., Meersman, R. (eds) On the Move to Meaningful Internet Systems. OTM 2018 Conferences. OTM 2018. Lecture Notes in Computer Science(), vol 11229. Springer, Cham.

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