Advertisement

Detecting Drift from Event Streams of Unpredictable Business Processes

  • Alireza OstovarEmail author
  • Abderrahmane Maaradji
  • Marcello La Rosa
  • Arthur H. M. ter Hofstede
  • Boudewijn F. V. van Dongen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9974)

Abstract

Existing business process drift detection methods do not work with event streams. As such, they are designed to detect inter-trace drifts only, i.e. drifts that occur between complete process executions (traces), as recorded in event logs. However, process drift may also occur during the execution of a process, and may impact ongoing executions. Existing methods either do not detect such intra-trace drifts, or detect them with a long delay. Moreover, they do not perform well with unpredictable processes, i.e. processes whose logs exhibit a high number of distinct executions to the total number of executions. We address these two issues by proposing a fully automated and scalable method for online detection of process drift from event streams. We perform statistical tests over distributions of behavioral relations between events, as observed in two adjacent windows of adaptive size, sliding along with the stream. An extensive evaluation on synthetic and real-life logs shows that our method is fast and accurate in the detection of typical change patterns, and performs significantly better than the state of the art.

Keywords

Window Size Business Process Change Pattern Concept Drift Event Stream 
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.

Notes

Acknowledgments

This research is partly funded by the Australian Research Council (grant DP150103356).

References

  1. 1.
    Carmona, J., Gavaldà, R.: Online techniques for dealing with concept drift in process mining. In: Hollmén, J., Klawonn, F., Tucker, A. (eds.) IDA 2012. LNCS, vol. 7619, pp. 90–102. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-34156-4_10 CrossRefGoogle Scholar
  2. 2.
    Accorsi, R., Stocker, T.: Discovering workflow changes with time-based trace clustering. In: Aberer, K., Damiani, E., Dillon, T. (eds.) SIMPDA 2011. LNBIP, vol. 116, pp. 154–168. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Bose, R.P.J.C., van der Aalst, W.M.P., Zliobaite, I., Pechenizkiy, M.: Dealing with concept drifts in process mining. IEEE Trans. NNLS 25(1), 154–171 (2014)Google Scholar
  4. 4.
    Martjushev, J., Bose, R.P.J.C., Aalst, W.M.P.: Change point detection and dealing with gradual and multi-order dynamics in process mining. In: Matulevičius, R., Dumas, M. (eds.) BIR 2015. LNBIP, vol. 229, pp. 161–178. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-21915-8_11 CrossRefGoogle Scholar
  5. 5.
    Maaradji, A., Dumas, M., Rosa, M., Ostovar, A.: Fast and accurate business process drift detection. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 406–422. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-23063-4_27 CrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    Burattin, A., Cimitile, M., Maggi, F.M., Sperduti, A.: Online discovery of declarative process models from event streams. IEEE Trans. Serv. Comput. 8, 833–846 (2015)CrossRefGoogle Scholar
  8. 8.
    Burattin, A., Sperduti, A., van der Aalst, W.M.P.: Control-flow discovery from event streams. In: IEEE Congress on Evolutionary Computation (CEC), pp. 2420–2427. IEEE (2014)Google Scholar
  9. 9.
    Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. (CSUR) 46(4), 1–37 (2014)CrossRefzbMATHGoogle Scholar
  10. 10.
    Leemans, S.J.J., Fahland, D., 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). doi: 10.1007/978-3-642-38697-8_17 CrossRefGoogle Scholar
  11. 11.
    de Medeiros, A.A., van Dongen, B.F., Van der Aalst, W.M.P., Weijters, A.: Process mining: extending the \(\alpha \)-algorithm to mine short loops. Technical report, BETA Working Paper Series, WP 113, Eindhoven University of Technology, Eindhoven (2004)Google Scholar
  12. 12.
    Harremoës, P., Tusnády, G.: Information divergence is more \(\chi ^{2} \)-distributed than the \(\chi ^{2} \)-statistics. In: IEEE ISIT, pp. 533–537 (2012)Google Scholar
  13. 13.
    Nuzzo, R.: Statistical errors. Nature 506(13), 150–152 (2014)CrossRefGoogle Scholar
  14. 14.
    Ho, S.S.: A martingale framework for concept change detection in time-varying data streams. In: Proceedings of ICML, pp. 321–327. ACM (2005)Google Scholar
  15. 15.
    Conforti, R., La Rosa, M., ter Hofstede, A.H.: Noise filtering of process execution logs based on outliers detection (2015)Google Scholar
  16. 16.
    Bifet, A., Gavaldà, R.: Kalman filters and adaptive windows for learning in data streams. In: Todorovski, L., Lavrač, N., Jantke, K.P. (eds.) DS 2006. LNCS (LNAI), vol. 4265, pp. 29–40. Springer, Heidelberg (2006). doi: 10.1007/11893318_7 CrossRefGoogle Scholar
  17. 17.
    Beest, N.R.T.P., Dumas, M., García-Bañuelos, L., Rosa, M.: Log delta analysis: interpretable differencing of business process event logs. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 386–405. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-23063-4_26 CrossRefGoogle Scholar
  18. 18.
    Pika, A., Wynn, M.T., Fidge, C.J., Hofstede, A.H.M., Leyer, M., Aalst, W.M.P.: An extensible framework for analysing resource behaviour using event logs. In: Jarke, M., Mylopoulos, J., Quix, C., Rolland, C., Manolopoulos, Y., Mouratidis, H., Horkoff, J. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 564–579. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-07881-6_38 Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Alireza Ostovar
    • 1
    Email author
  • Abderrahmane Maaradji
    • 1
  • Marcello La Rosa
    • 1
  • Arthur H. M. ter Hofstede
    • 1
    • 2
  • Boudewijn F. V. van Dongen
    • 2
  1. 1.Queensland University of TechnologyBrisbaneAustralia
  2. 2.Eindhoven University of TechnologyEindhovenThe Netherlands

Personalised recommendations