Advertisement

Clustering Event Traces by Behavioral Similarity

  • Agnes KoschmiderEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10651)

Abstract

The automated analysis of event logs in smart homes could provide an IT-aided support for a highly autonomous and age-appropriate life standard. The analysis of human behavior in the context of smart homes is, however, a challenging task. Humans behave according to the best practices and a single behavioral model is typically not sufficient to represent them all. In fact, existing process mining algorithms reportedly generate spaghetti models from event logs of flexible processes, which are largely incomprehensible. Therefore, this paper presents a novel approach for clustering event traces by their behavioral similarity, rather than deriving a unique process model encompassing all traces. In order to do this, two algorithms are introduced and we report the results of a preliminary evaluation demonstrating the efficacy of the approach.

References

  1. 1.
    Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: A genetic algorithm for discovering process trees. In: IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2012)Google Scholar
  2. 2.
    Fahland, D., van der Aalst, W.M.: Model repair - aligning process models to reality. Inf. Syst. 47, 220–243 (2015)CrossRefGoogle Scholar
  3. 3.
    Ehrig, M., Koschmider, A., Oberweis, A.: Measuring similarity between semantic business process models. In: APCCM. CRPIT, Vol. 67, pp. 71–80. Australian Computer Society (2007)Google Scholar
  4. 4.
    Koschmider, A., Ullrich, M., Heine, A., Oberweis, A.: Revising the vocabulary of business process element labels. In: Zdravkovic, J., Kirikova, M., Johannesson, P. (eds.) CAiSE 2015. LNCS, vol. 9097, pp. 69–83. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-19069-3_5 CrossRefGoogle Scholar
  5. 5.
    Jouck, T., Depaire, B.: PTandLoggenerator: A generator for artificial event data. In: Proceedings of the BPM Demo Track 2016, CEUR-WS.org, pp. 23–27 (2016)Google Scholar
  6. 6.
    Song, M., Günther, C.W., van der Aalst, W.M.P.: Trace clustering in process mining. In: Ardagna, D., Mecella, M., Yang, J. (eds.) BPM 2008. LNBIP, vol. 17, pp. 109–120. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-00328-8_11 CrossRefGoogle Scholar
  7. 7.
    Bose, R.P.J.C., van der Aalst, W.M.P.: Context aware trace clustering: towards improving process mining results. In: Proceedings of the SIAM International Conference on Data Mining (SDM 2009), pp. 401–412 (2009)Google Scholar
  8. 8.
    Hompes, B., Buijs, J.C.A.M., van der Aalst, W.M.P., Dixit, P., Buurman, H.: Detecting change in processes using comparative trace clustering. In: Proceedings of the 5th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2015), Vienna, Austria, 9–11 December 2015, pp. 95–108 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute AIFBKarlsruhe Institute of TechnologyKarlsruheGermany

Personalised recommendations