Advertisement

Decision Mining Revisited - Discovering Overlapping Rules

  • Felix MannhardtEmail author
  • Massimiliano de Leoni
  • Hajo A. Reijers
  • Wil M. P. van der Aalst
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9694)

Abstract

Decision mining enriches process models with rules underlying decisions in processes using historical process execution data. Choices between multiple activities are specified through rules defined over process data. Existing decision mining methods focus on discovering mutually-exclusive rules, which only allow one out of multiple activities to be performed. These methods assume that decision making is fully deterministic, and all factors influencing decisions are recorded. In case the underlying decision rules are overlapping due to non-determinism or incomplete information, the rules returned by existing methods do not fit the recorded data well. This paper proposes a new technique to discover overlapping decision rules, which fit the recorded data better at the expense of precision, using decision tree learning techniques. An evaluation of the method on two real-life data sets confirms this trade off. Moreover, it shows that the method returns rules with better fitness and precision in under certain conditions.

Keywords

Decision mining Process mining Overlapping rules 

References

  1. 1.
    van der Aalst, W.M.P.: Process Mining - Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)zbMATHGoogle Scholar
  2. 2.
    Object Management Group (OMG): Decision Model And Notation (DMN) Version 1.0, formal/2015-09-01 (2015)Google Scholar
  3. 3.
    Rozinat, A., van der Aalst, W.M.P.: Decision mining in ProM. In: Dustdar, S., Fiadeiro, J.L., Sheth, A.P. (eds.) BPM 2006. LNCS, vol. 4102, pp. 420–425. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    de Leoni, M., van der Aalst, W.M.P.: Data-aware process mining: discovering decisions in processes using alignments. In: SAC 2013, pp. 1454–1461. ACM (2013)Google Scholar
  5. 5.
    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)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Grigori, D., Casati, F., Castellanos, M., Dayal, U., Sayal, M., Shan, M.C.: Business process intelligence. Comput. Ind. 53(3), 321–343 (2004)CrossRefGoogle Scholar
  7. 7.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)Google Scholar
  8. 8.
    Rosca, D., Wild, C.: Towards a flexible deployment of business rules. Expert Syst. Appl. 23(4), 385–394 (2002)CrossRefGoogle Scholar
  9. 9.
    Bose, R.P.J.C., Mans, R.S., van der Aalst, W.M.P.: Wanna improve process mining results? In: IEEE Symposium on Computational Intelligence and Data Mining, pp. 127–134 (2013)Google Scholar
  10. 10.
    Desel, J., Esparza, J.: Free Choice Petri Nets. Cambridge University Press, New York (1995)CrossRefzbMATHGoogle Scholar
  11. 11.
    van der Aalst, W.M.P., Adriansyah, A., van Dongen, B.F.: Replaying history on process models for conformance checking and performance analysis. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2(2), 182–192 (2012)CrossRefGoogle Scholar
  12. 12.
    Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P.: Measuring the precision of multi-perspective process models. In: Business Process Management Workshops - BPM 2015 (2015, to appear)Google Scholar
  13. 13.
    Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P.: Decision mining revisited - discovering overlapping rules. Technical report, BPMcenter.org, BPM Center Report BPM-01-06 (2016)Google Scholar
  14. 14.
    Mannhardt, F., de Leoni, M., Reijers, H.A.: The multi-perspective process explorer. In: BPM Demo Session 2015, vol. 1418, pp. 130–134. CEUR-WS.org (2015)Google Scholar
  15. 15.
    de Leoni, M., Mannhardt, F.: Road traffic fine management process. Eindhoven University ofTechnology. Dataset (2015). doi: 10.4121/uuid:270fd440-1057-4fb9-89a9-b699b47990f5
  16. 16.
    Jareevongpiboon, W., Janecek, P.: Ontological approach to enhance results of business process mining and analysis. Bus. Process. Manag. J. 19(3), 459–476 (2013)CrossRefGoogle Scholar
  17. 17.
    Catalkaya, S., Knuplesch, D., Chiao, C., Reichert, M.: Enriching business process models with decision rules. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013 Workshops. LNBIP, vol. 171, pp. 198–211. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  18. 18.
    Ghattas, J., Soffer, P., Peleg, M.: Improving business process decision making based on past experience. Decis. Support Syst. 59, 93–107 (2014)CrossRefGoogle Scholar
  19. 19.
    Bazhenova, E., Weske, M.: Deriving decision models from process models by enhanced decision mining. In: Business Process Management Workshops - BPM 2015 (2015, to appear)Google Scholar
  20. 20.
    Dunkl, R., Rinderle-Ma, S., Grossmann, W., Anton Fröschl, K.: A method for analyzing time series data in process mining: application and extension of decision point analysis. In: Nurcan, S., Pimenidis, E. (eds.) CAiSE Forum 2014. LNBIP, vol. 204, pp. 68–84. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  21. 21.
    de Leoni, M., Dumas, M., García-Bañuelos, L.: Discovering branching conditions from business process execution logs. In: Cortellessa, V., Varró, D. (eds.) FASE 2013 (ETAPS 2013). LNCS, vol. 7793, pp. 114–129. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  22. 22.
    Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recogn. 37(9), 1757–1771 (2004)CrossRefGoogle Scholar
  23. 23.
    Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehouse. Min. 2007, 1–13 (2007)CrossRefGoogle Scholar
  24. 24.
    Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 333–359 (2011)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22(2), 207–216 (1993)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Felix Mannhardt
    • 1
    • 2
    Email author
  • Massimiliano de Leoni
    • 1
  • Hajo A. Reijers
    • 1
    • 3
  • Wil M. P. van der Aalst
    • 1
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Lexmark Enterprise SoftwareNaardenThe Netherlands
  3. 3.Vrije Universiteit AmsterdamAmsterdamThe Netherlands

Personalised recommendations