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PM\(^2\): A Process Mining Project Methodology

  • Maikel L. van EckEmail author
  • Xixi Lu
  • Sander J. J. Leemans
  • Wil M. P. van der Aalst
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9097)

Abstract

Process mining aims to transform event data recorded in information systems into knowledge of an organisation’s business processes. The results of process mining analysis can be used to improve process performance or compliance to rules and regulations. However, applying process mining in practice is not trivial. In this paper we introduce PM\(^2\), a methodology to guide the execution of process mining projects. We successfully applied PM\(^2\) during a case study within IBM, a multinational technology corporation, where we identified potential process improvements for one of their purchasing processes.

Keywords

Process mining Methodology Case study Business process management. 

References

  1. 1.
    van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer (2011)Google Scholar
  2. 2.
    van der Aalst, W.M.P.: Process cubes: slicing, dicing, rolling up and drilling down event data for process mining. In: Song, M., Wynn, M.T., Liu, J. (eds.) AP-BPM 2013. LNBIP, vol. 159, pp. 1–22. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  3. 3.
    van der Aalst, W.M.P., Adriansyah, A., van Dongen, B.F.: Replaying history on process models for conformance checking and performance analysis. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2(2), 182–192 (2012)Google Scholar
  4. 4.
    Bose, R.P.J.C., Mans, R.S., van der Aalst, W.M.P.: Wanna improve process mining results? In: 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 127–134. IEEE (2013)Google Scholar
  5. 5.
    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
  6. 6.
    Bozkaya, M., Gabriels, J., Werf, J.: Process diagnostics: a method based on process mining. In: International Conference on Information, Process, and Knowledge Management, eKNOW 2009, pp. 22–27. IEEE (2009)Google Scholar
  7. 7.
    De Weerdt, J., De Backer, M., Vanthienen, J., Baesens, B.: A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Information Systems 37(7), 654–676 (2012)CrossRefGoogle Scholar
  8. 8.
    Harmon, P.: Business process change: A guide for business managers and BPM and Six Sigma professionals. Morgan Kaufmann (2010)Google Scholar
  9. 9.
    Kurgan, L.A., Musilek, P.: A Survey of Knowledge Discovery and Data Mining Process Models. The Knowledge Engineering Review 21(01), 1–24 (2006)CrossRefGoogle Scholar
  10. 10.
    Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Exploring processes and deviations. In: Fournier, F., Mendling, J. (eds.) BPM 2014 Workshops. LNBIP, vol. 202, pp. 304–316. Springer, Heidelberg (2015) CrossRefGoogle Scholar
  11. 11.
    de Leoni, M., van der Aalst, W.M.P., Dees, M.: A general framework for correlating business process characteristics. In: Sadiq, S., Soffer, P., Völzer, H. (eds.) BPM 2014. LNCS, vol. 8659, pp. 250–266. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  12. 12.
    Mariscal, G., Marbán, Ó., Fernández, C.: A Survey of Data Mining and Knowledge Discovery Process Models and Methodologies. The Knowledge Engineering Review 25(02), 137–166 (2010)CrossRefGoogle Scholar
  13. 13.
    Nooijen, E.H.J., van Dongen, B.F., Fahland, D.: Automatic discovery of data-centric and artifact-centric processes. In: La Rosa, M., Soffer, P. (eds.) BPM Workshops 2012. LNBIP, vol. 132, pp. 316–327. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  14. 14.
    Rebuge, Á., Ferreira, D.R.: Business Process Analysis in Healthcare Environments: a Methodology based on Process Mining. Information Systems 37(2), 99–116 (2012)CrossRefGoogle Scholar
  15. 15.
    Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)CrossRefGoogle Scholar
  16. 16.
    Shearer, C.: The crisp-dm model: the new blueprint for data mining. Journal of data warehousing 5(4), 13–22 (2000)Google Scholar
  17. 17.
    Song, M., van der Aalst, W.M.P.: Supporting process mining by showing events at a glance. In: Workshop on Information Technologies and Systems, pp. 139–145 (2007)Google Scholar
  18. 18.
    Suriadi, S., Wynn, M.T., Ouyang, C., ter Hofstede, A.H.M., van Dijk, N.J.: Understanding process behaviours in a large insurance company in australia: a case study. In: Salinesi, C., Norrie, M.C., Pastor, Ó. (eds.) CAiSE 2013. LNCS, vol. 7908, pp. 449–464. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  19. 19.
    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) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Maikel L. van Eck
    • 1
    Email author
  • Xixi Lu
    • 1
  • Sander J. J. Leemans
    • 1
  • Wil M. P. van der Aalst
    • 1
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands

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