From Low-Level Events to Activities - A Pattern-Based Approach

  • Felix Mannhardt
  • Massimiliano de Leoni
  • Hajo A. Reijers
  • Wil M. P. van der Aalst
  • Pieter J. Toussaint
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9850)


Process mining techniques analyze processes based on event data. A crucial assumption for process analysis is that events correspond to occurrences of meaningful activities. Often, low-level events recorded by information systems do not directly correspond to these. Abstraction methods, which provide a mapping from the recorded events to activities recognizable by process workers, are needed. Existing supervised abstraction methods require a full model of the entire process as input and cannot handle noise. This paper proposes a supervised abstraction method based on behavioral activity patterns that capture domain knowledge on the relation between activities and events. Through an alignment between the activity patterns and the low-level event logs an abstracted event log is obtained. Events in the abstracted event log correspond to instantiations of recognizable activities. The method is evaluated with domain experts of a Norwegian hospital using an event log from their digital whiteboard system. The evaluation shows that state-of-the art process mining methods provide valuable insights on the usage of the system when using the abstracted event log, but fail when using the original lower level event log.


Process mining Supervised abstraction Event log Alignment 



We would like to thank Ivar Myrstad for his valuable insights on the digital whiteboard and his help with the case study.


  1. 1.
    van der Aalst, W.M.P.: Process Mining - Discovery, Conformance and Enhancement of Business Processes. Springer, Berlin (2011)MATHGoogle Scholar
  2. 2.
    Günther, C.W., Rozinat, A., van der Aalst, W.M.P.: Activity mining by global trace segmentation. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 128–139. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Baier, T., Mendling, J., Weske, M.: Bridging abstraction layers in process mining. Inf. Syst. 46, 123–139 (2014)CrossRefGoogle Scholar
  4. 4.
    Jagadeesh Chandra Bose, R.P., van der Aalst, W.M.P.: Abstractions in process mining: a taxonomy of patterns. In: Dayal, U., Eder, J., Koehler, J., Reijers, H.A. (eds.) BPM 2009. LNCS, vol. 5701, pp. 159–175. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Cook, D.J., Krishnan, N.C., Rashidi, P.: Activity discovery and activity recognition: a new partnership. IEEE Trans. Cybern. 43(3), 820–828 (2013)CrossRefGoogle Scholar
  6. 6.
    Ferreira, D.R., Szimanski, F., Ralha, C.G.: Improving process models by mining mappings of low-level events to high-level activities. J. Intell. Inf. Syst. 43(2), 379–407 (2014)CrossRefGoogle Scholar
  7. 7.
    Folino, F., Guarascio, M., Pontieri, L.: Mining multi-variant process models from low-level logs. In: Abramowicz, W. (ed.) BIS 2015. LNBIP, vol. 208, pp. 165–177. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  8. 8.
    Baier, T., Rogge-Solti, A., Mendling, J., Weske, M.: Matching of events and activities: an approach based on behavioral constraint satisfaction. In: SAC, pp. 1225–1230. ACM (2015)Google Scholar
  9. 9.
    Ferreira, D.R., Szimanski, F., Ralha, C.G.: Mining the low-level behaviour of agents in high-level business processes. IJBPIM 6(2), 146–166 (2013)CrossRefGoogle Scholar
  10. 10.
    Fazzinga, B., Flesca, S., Furfaro, F., Masciari, E., Pontieri, L.: A probabilistic unified framework for event abstraction and process detection from log data. In: Debruyne, C., Panetto, H., Meersman, R., Dillon, T., Weichhart, G., An, Y., Ardagna, C.A. (eds.) OTM 2015 Conferences. LNCS, vol. 9415, pp. 320–328. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  11. 11.
    Baier, T.: Matching events and activities. Ph.D. thesis, Universität Potsdam (2015)Google Scholar
  12. 12.
    Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P.: Balanced multi-perspective checking of process conformance. Computing 98(4), 407–437 (2016)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P., Toussaint, P.J.: From low-level events to activities - a pattern-based approach. Technical report,, BPM Center Report BPM-02-06 (2016)Google Scholar
  14. 14.
    Wong, H.J., Caesar, M., Bandali, S., Agnew, J., Abrams, H.: Electronic inpatient whiteboards: improving multidisciplinary communication and coordination of care. Int. J. Med. Inform. 78(4), 239–247 (2009)CrossRefGoogle Scholar
  15. 15.
    Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Using life cycle information in process discovery. In: Reichert, M., Reijers, H. (eds.) BPM Workshops 2015. LNBIP, vol. 256, pp. 204–217. Springer, Heidelberg (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Felix Mannhardt
    • 1
  • Massimiliano de Leoni
    • 1
  • Hajo A. Reijers
    • 1
    • 2
  • Wil M. P. van der Aalst
    • 1
  • Pieter J. Toussaint
    • 3
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Vrije Universiteit AmsterdamAmsterdamThe Netherlands
  3. 3.Norwegian University of Science and TechnologyTrondheimNorway

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