Process Compliance Measurement Based on Behavioural Profiles

  • Matthias Weidlich
  • Artem Polyvyanyy
  • Nirmit Desai
  • Jan Mendling
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6051)

Abstract

Process compliance measurement is getting increasing attention in companies due to stricter legal requirements and market pressure for operational excellence. On the other hand, the metrics to quantify process compliance have only been defined recently. A major criticism points to the fact that existing measures appear to be unintuitive. In this paper, we trace back this problem to a more foundational question: which notion of behavioural equivalence is appropriate for discussing compliance? We present a quantification approach based on behavioural profiles, which is a process abstraction mechanism. Behavioural profiles can be regarded as weaker than existing equivalence notions like trace equivalence, and they can be calculated efficiently. As a validation, we present a respective implementation that measures compliance of logs against a normative process model. This implementation is being evaluated in a case study with an international service provider.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    van der Aalst, W., Reijers, H., Weijters, A., van Dongen, B., de Medeiros, A., Song, M., Verbeek, H.: Business process mining: An industrial application. Inf. Syst. 32(5), 713–732 (2007)CrossRefGoogle Scholar
  2. 2.
    de Medeiros, A., van der Aalst, W., Weijters, A.: Quantifying process equivalence based on observed behavior. Data Knowl. Eng. 64(1), 55–74 (2008)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Glabbeek, R.: The Linear Time – Brancing Time Spectrum I. The semantics of concrete, sequential processes. In: Handbook of Process Algebra. Elsevier, Amsterdam (2001)Google Scholar
  5. 5.
    Valmari, A.: The state explosion problem. In: Reisig, W., Rozenberg, G. (eds.) APN 1998. LNCS, vol. 1491, pp. 429–528. Springer, Heidelberg (1998)Google Scholar
  6. 6.
    Gerke, K., Cardoso, J., Claus, A.: Measuring the compliance of processes with reference models. In: Meersman, R., Dillon, T., Herrero, P. (eds.) OTM 2009, Part I, LNCS, vol. 5870, pp. 76–93. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Weidlich, M., Mendling, J., Weske, M.: Computation of behavioural profiles of process models. Technical report, Hasso Plattner Institute (June 2009)Google Scholar
  8. 8.
    Aalst, W., Weijters, A., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)CrossRefGoogle Scholar
  9. 9.
    Küster, J.M., Gerth, C., Förster, A., Engels, G.: Detecting and resolving process model differences in the absence of a change log. In: [20], pp. 244–260Google Scholar
  10. 10.
    Dijkman, R.M.: Diagnosing differences between business process models. In: [20], pp. 261–277Google Scholar
  11. 11.
    Glabbeek, R., Goltz, U.: Refinement of actions and equivalence notions for concurrent systems. Acta Inf. 37(4/5), 229–327 (2001)MATHCrossRefGoogle Scholar
  12. 12.
    Hidders, J., Dumas, M., Aalst, W., Hofstede, A., Verelst, J.: When are two workflows the same? In: CATS. CRPIT, vol. 41, pp. 3–11 (2005)Google Scholar
  13. 13.
    Basten, T., Aalst, W.: Inheritance of behavior. JLAP 47(2), 47–145 (2001)MATHGoogle Scholar
  14. 14.
    Ebert, J., Engels, G.: Observable or Invocable Behaviour - You Have to Choose. Technical Report 94-38, Leiden University (December 1994)Google Scholar
  15. 15.
    Schrefl, M., Stumptner, M.: Behavior-consistent specialization of object life cycles. ACM Trans. Softw. Eng. Methodol. 11(1), 92–148 (2002)CrossRefGoogle Scholar
  16. 16.
    Wombacher, A.: Evaluation of technical measures for workflow similarity based on a pilot study. In: Meersman, R., Tari, Z. (eds.) OTM 2006, Part I, LNCS, vol. 4275, pp. 255–272. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Wombacher, A., Rozie, M.: Evaluation of workflow similarity measures in service discovery. In: Service Oriented Electronic Commerce. LNI, vol. 80, pp. 51–71 (2006)Google Scholar
  18. 18.
    Li, C., Reichert, M., Wombacher, A.: On measuring process model similarity based on high-level change operations. In: Li, Q., Spaccapietra, S., Yu, E., Olivé, A. (eds.) ER 2008. LNCS, vol. 5231, pp. 248–264. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Dongen, B., Dijkman, R.M., Mendling, J.: Measuring similarity between business process models. In: Bellahsène, Z., Léonard, M. (eds.) CAiSE 2008. LNCS, vol. 5074, pp. 450–464. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  20. 20.
    Dumas, M., Reichert, M., Shan, M.-C. (eds.): BPM 2008. LNCS, vol. 5240. Springer, Heidelberg (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Matthias Weidlich
    • 1
  • Artem Polyvyanyy
    • 1
  • Nirmit Desai
    • 2
  • Jan Mendling
    • 3
  1. 1.Hasso Plattner Institute at the University of PotsdamGermany
  2. 2.IBM India Research LabsIndia
  3. 3.Humboldt-Universität zu BerlinGermany

Personalised recommendations