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Handling Concept Drift in Process Mining

  • R. P. Jagadeesh Chandra Bose
  • Wil M. P. van der Aalst
  • Indrė Žliobaitė
  • Mykola Pechenizkiy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6741)

Abstract

Operational processes need to change to adapt to changing circumstances, e.g., new legislation, extreme variations in supply and demand, seasonal effects, etc. While the topic of flexibility is well-researched in the BPM domain, contemporary process mining approaches assume the process to be in steady state. When discovering a process model from event logs, it is assumed that the process at the beginning of the recorded period is the same as the process at the end of the recorded period. Obviously, this is often not the case due to the phenomenon known as concept drift. While cases are being handled, the process itself may be changing. This paper presents an approach to analyze such second-order dynamics. The approach has been implemented in ProM and evaluated by analyzing an evolving process.

Keywords

process mining concept drift flexibility change patterns 

References

  1. 1.
    Žliobaitė, I.: Learning under Concept Drift: an Overview. Technical report, Faculty of Mathematics and Informatics, Vilnius University, Vilnius, Lithuania (2009)Google Scholar
  2. 2.
    Pechenizkiy, M., Bakker, J., Žliobaitė, I., Ivannikov, A., Kärkkäinen, T.: Online Mass Flow Prediction in CFB Boilers with Explicit Detection of Sudden Concept Drift. SIGKDD Explorations 11(2), 109–116 (2009)CrossRefGoogle Scholar
  3. 3.
    Tsymbal, A., Pechenizkiy, M., Cunningham, P., Puuronen, S.: Handling Local Concept Drift with Dynamic Integration of Classifiers: Domain of Antibiotic Resistance in Nosocomial Infections. In: CBMS, pp. 679–684 (2006)Google Scholar
  4. 4.
    Weber, B., Rinderle, S., Reichert, M.: Change Patterns and Change Support Features in Process-Aware Information Systems. In: Krogstie, J., Opdahl, A.L., Sindre, G. (eds.) CAiSE 2007 and WES 2007. LNCS, vol. 4495, pp. 574–588. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Mulyar, N.: Patterns for Process-Aware Information Systems: An Approach Based on Colored Petri Nets. PhD thesis, University of Technology, Eindhoven (2009)Google Scholar
  6. 6.
    Schonenberg, H., Mans, R., Russell, N., Mulyar, N., van der Aalst, W.M.P.: Process Flexibility: A Survey of Contemporary Approaches. In: Dietz, J., Albani, A., Barjis, J. (eds.) Advances in Enterprise Engineering I. LNCS, vol. 10, pp. 16–30. Springer, Berlin (2008)CrossRefGoogle Scholar
  7. 7.
    Regev, G., Soffer, P., Schmidt, R.: Taxonomy of Flexibility in Business Processes. In: Proceedings of the 7th Workshop on Business Process Modelling, Development and Support, BPMDS, Citeseer (2006)Google Scholar
  8. 8.
    Ploesser, K., Recker, J.C., Rosemann, M.: Towards a Classification and Lifecycle of Business Process Change. In: Proceedings of BPMDS, vol. 8 (2008)Google Scholar
  9. 9.
    Günther, C.W., Rinderle-Ma, S., Reichert, M., van der Aalst, W.M.P.: Using Process Mining to Learn from Process Changes in Evolutionary Systems. International Journal of Business Process Integration and Management 3(1), 61–78 (2008)CrossRefGoogle Scholar
  10. 10.
    Widmer, G., Kubat, M.: Learning in the Presence of Concept Drift and Hidden Contexts. Machine learning 23(1), 69–101 (1996)Google Scholar
  11. 11.
    Smyth, P., Goodman, R.M.: Rule Induction Using Information Theory. In: Knowledge Discovery in Databases, pp. 159–176. AAAI Press, Menlo Park (1991)Google Scholar
  12. 12.
    Blachman, N.M.: The Amount of Information that y Gives About X. IEEE Transactions on Information Theory IT-14(1), 27–31 (1968)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Sheskin, D.: Handbook of Parametric and Nonparametric Statistical Procedures. Chapman & Hall/CRC (2004)Google Scholar
  14. 14.
    van der Aalst, W.M.P., ter Hofstede, A.H.M.: YAWL: Yet Another Workflow Language. Information Systems 30(4), 245–275 (2005)CrossRefGoogle Scholar
  15. 15.
    Vinter Ratzer, A., Wells, L., Lassen, H.M., Laursen, M., Qvortrup, J.F., Stissing, M.S., Westergaard, M., Christensen, S., Jensen, K.: CPN Tools for Editing, Simulating, and Analysing Coloured Petri Nets. In: van der Aalst, W.M.P., Best, E. (eds.) ICATPN 2003. LNCS, vol. 2679, pp. 450–462. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  16. 16.
    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

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • R. P. Jagadeesh Chandra Bose
    • 1
    • 2
  • Wil M. P. van der Aalst
    • 1
  • Indrė Žliobaitė
    • 1
  • Mykola Pechenizkiy
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of TechnologyEindhovenThe Netherlands
  2. 2.Philips HealthcareBestThe Netherlands

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