An Improved Way for Measuring Simplicity During Process Discovery

  • Jonas LiebenEmail author
  • Toon Jouck
  • Benoît Depaire
  • Mieke Jans
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 332)


In the domain of process discovery, there are four quality dimensions for evaluating process models of which simplicity is one. Simplicity is often measured using the size of a process model, the structuredness and the entropy. It is closely related to the process model understandability. Researchers from the domain of business process management (BPM) proposed several metrics for measuring the process model understandability. A part of these understandability metrics focus on the control-flow perspective, which is important for evaluating models from process discovery algorithms. It is remarkable that there are more of these metrics defined in the BPM literature compared to the number of proposed simplicity metrics. To research whether the understandability metrics capture more understandability dimensions than the simplicity metrics, an exploratory factor analysis was conducted on 18 understandability metrics. A sample of 4450 BPMN models, both manually modelled and artificially generated, is used. Four dimensions are discovered: token behaviour complexity, node IO complexity, path complexity and degree of connectedness. The conclusion of this analysis is that process analysts should be aware that the measurement of simplicity does not capture all dimensions of the understandability of process models.


Understandability metrics Simplicity Process models Exploratory factor analysis BPMN 


  1. 1.
    van der Aalst, W.M.P.: Business process management: a comprehensive survey. ISRN Softw. Eng. 2013, 1–37 (2013)CrossRefGoogle Scholar
  2. 2.
    van der Aalst, W., et al.: Business process mining: an industrial application. Inf. Syst. 32(5), 713–732 (2007)CrossRefGoogle Scholar
  3. 3.
    Reijers, H.A., Mendling, J.: A study into the factors that influence the understandability of business process models. IEEE Trans. Syst. Man, Cybern.-Part A: Syst. Hum. 41(3), 449–462 (2011)CrossRefGoogle Scholar
  4. 4.
    van der Aalst, W.M.P.: Process Mining: Discovery Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011). Scholar
  5. 5.
    Lassen, K.B., van der Aalst, W.M.: Complexity metrics for workflow nets. Inf. Softw. Technol. 51(3), 610–626 (2009)CrossRefGoogle Scholar
  6. 6.
    Ihaka, R., Gentleman, R.: R: a language for data analysis and graphics. J. Comput. Graph. Stat. 5(3), 299–314 (1996)Google Scholar
  7. 7.
    Sarshar, K., Loos, P.: Comparing the control-flow of EPC and petri net from the end-user perspective. In: van der Aalst, W.M.P., Benatallah, B., Casati, F., Curbera, F. (eds.) BPM 2005. LNCS, vol. 3649, pp. 434–439. Springer, Heidelberg (2005). Scholar
  8. 8.
    Recker, J.C., Dreiling, A.: Does it matter which process modelling language we teach or use? An experimental study on understanding process modelling languages without formal education, Toowoomba (2007)Google Scholar
  9. 9.
    Laue, R., Gruhn, V.: Complexity metrics for business process models. In: Business Information Systems, Klagenfurt, Austria, January 2006Google Scholar
  10. 10.
    Petrusel, R., Mendling, J., Reijers, H.A.: How visual cognition influences process model comprehension. Decis. Support Syst. 96(Suppl. C), 1–16 (2017)CrossRefGoogle Scholar
  11. 11.
    Mendling, J.: Detection and prediction of errors in EPC business process models. PhD thesis, Wirtschaftsuniversitt Wien Vienna (2007)Google Scholar
  12. 12.
    Fernndez-Ropero, M., Prez-Castillo, R., Caballero, I., Piattini, M.: Quality-driven business process refactoring. In: International Conference on Business Information Systems (ICBIS 2012), pp. 960–966 (2012)Google Scholar
  13. 13.
    Mendling, J., Strembeck, M.: Influence factors of understanding business process models. In: Abramowicz, W., Fensel, D. (eds.) BIS 2008. LNBIP, vol. 7, pp. 142–153. Springer, Heidelberg (2008). Scholar
  14. 14.
    Cardoso, J.: Control-flow complexity measurement of processes and Weyuker’s properties. In: 6th International Enformatika Conference. vol. 8, pp. 213–218 (2005)Google Scholar
  15. 15.
    Figl, K.: Comprehension of procedural visual business process models: a literature review. Bus. Inf. Syst. Eng. 59(1), 41–67 (2017)CrossRefGoogle Scholar
  16. 16.
    Polani, G., Cegnar, B.: Complexity metrics for process models a systematic literature review. Comput. Stand. Interfaces 51(Suppl. C), 104–117 (2017)Google Scholar
  17. 17.
    Gschwind, T., Koehler, J., Wong, J.: Applying patterns during business process modeling. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 4–19. Springer, Heidelberg (2008). Scholar
  18. 18.
    Pavlicek, J., Hronza, R., Pavlickova, P., Jelinkova, K.: The business process model quality metrics. In: Pergl, R., Lock, R., Babkin, E., Molhanec, M. (eds.) EOMAS 2017. LNBIP, vol. 298, pp. 134–148. Springer, Cham (2017). Scholar
  19. 19.
    Gruhn, V., Laue, R.: Reducing the cognitive complexity of business process models, pp. 339–345, June 2009Google Scholar
  20. 20.
    La Rosa, M., Wohed, P., Mendling, J., Ter Hofstede, A.H., Reijers, H.A., van der Aalst, W.M.: Managing process model complexity via abstract syntax modifications. IEEE Trans. Ind. Inf. 7(4), 614–629 (2011)CrossRefGoogle Scholar
  21. 21.
    Muketha, G.: Complexity metrics for measuring the understandability and maintainability of business process models using goal-question-metric (GQM). Int. J. Comput. Sci. Netw. Secur. 8(5), 219–225 (2008)Google Scholar
  22. 22.
    Vanderfeesten, I., Reijers, H.A., Mendling, J., van der Aalst, W.M.P., Cardoso, J.: On a quest for good process models: the cross-connectivity metric. In: Bellahsène, Z., Léonard, M. (eds.) CAiSE 2008. LNCS, vol. 5074, pp. 480–494. Springer, Heidelberg (2008). Scholar
  23. 23.
    Kunze, M., Berger, P., Weske, M., Lohmann, N., Moser, S.: BPM academic initiative-fostering empirical research. In: BPM, pp. 1–5 Demos (2012)Google Scholar
  24. 24.
    Jouck, T., Depaire, B.: Generating artificial data for empirical analysis of control-flow discovery algorithms: a process tree and log generator. Bus. Inf. Syst. Eng. 10, 18 (2018)Google Scholar
  25. 25.
    van der Aalst, W.: On the representational bias in process mining, pp. 2–7. IEEE, June 2011Google Scholar
  26. 26.
    Hair, J., Black, W., Babin, B., Anderson, R.: Multivariate Data Analysis, Number Seventh edn. Pearson Education Limited, London (2013)Google Scholar
  27. 27.
    Child, D.: The Essentials of Factor Analysis. A&C Black, London (2006). Google-Books-ID: rQ2vdJgohH0CGoogle Scholar
  28. 28.
    Sim, K.A., Tan, T.S.: Wong, K.B.: On the shortest path in some k-connected graphs, p. 050010 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jonas Lieben
    • 1
    • 2
    Email author
  • Toon Jouck
    • 1
  • Benoît Depaire
    • 1
  • Mieke Jans
    • 1
  1. 1.Hasselt UniversityHasseltBelgium
  2. 2.FWOBrusselBelgium

Personalised recommendations