On the Performance Overhead of BPMN Modeling Practices

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10445)

Abstract

Business process models can serve different purposes, from discussion and analysis among stakeholders, to simulation and execution. While work has been done on deriving modeling guidelines to improve understandability, it remains to be determined how different modeling practices impact the execution of the models. In this paper we observe how semantically equivalent, but syntactically different, models behave in order to assess the performance impact of different modeling practices. To do so, we propose a methodology for systematically deriving semantically equivalent models by applying a set of model transformation rules and for precisely measuring their execution performance. We apply the methodology on three scenarios to systematically explore the performance variability of 16 different versions of parallel, exclusive, and inclusive control flows. Our experiments with two open-source business process management systems measure the execution duration of each model’s instances. The results reveal statistically different execution performance when applying different modeling practices without total ordering of performance ranks.

Keywords

BPMN 2.0 Execution performance Semantic equivalence 

References

  1. 1.
    Aalst, W.M.P., Medeiros, A.K.A., Weijters, A.J.M.M.: Process equivalence: comparing two process models based on observed behavior. In: Dustdar, S., Fiadeiro, J.L., Sheth, A.P. (eds.) BPM 2006. LNCS, vol. 4102, pp. 129–144. Springer, Heidelberg (2006). doi:10.1007/11841760_10 CrossRefGoogle Scholar
  2. 2.
    Abbott, M.L., Fisher, M.T.: The Art of Scalability. Pearson, Upper Saddle River (2009)Google Scholar
  3. 3.
    Bacon, D.F., Graham, S.L., Sharp, O.J.: Compiler transformations for high-performance computing. ACM Comput. Surv. (CSUR) 26(4), 345–420 (1994)CrossRefGoogle Scholar
  4. 4.
    Cohen, J.: A power primer. Psychol. Bull. 112(1), 55 (1992)CrossRefGoogle Scholar
  5. 5.
    Dattalo, P.: Determining Sample Size: Balancing Power, Precision, and Practicality. Oxford University Press, New York (2008)CrossRefGoogle Scholar
  6. 6.
    Dinno, A.: Nonparametric pairwise multiple comparisons in independent groups using dunns test. Stata J. 15, 292–300 (2015)Google Scholar
  7. 7.
    Dumas, M., Rosa, M., Mendling, J., Mäesalu, R., Reijers, H.A., Semenenko, N.: Understanding business process models: the costs and benefits of structuredness. In: Ralyté, J., Franch, X., Brinkkemper, S., Wrycza, S. (eds.) CAiSE 2012. LNCS, vol. 7328, pp. 31–46. Springer, Heidelberg (2012). doi:10.1007/978-3-642-31095-9_3 CrossRefGoogle Scholar
  8. 8.
    Eder, J., Gruber, W., Pichler, H.: Transforming workflow graphs. In: Konstantas, D., Bourrières, J.P., Léonard, M., Boudjlida, N. (eds.) Interoperability of Enterprise Software and Applications, pp. 203–214. Springer, London (2006)CrossRefGoogle Scholar
  9. 9.
    Ferme, V., Ivanchikj, A., Pautasso, C.: A framework for benchmarking BPMN 2.0 workflow management systems. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 251–259. Springer, Cham (2015). doi:10.1007/978-3-319-23063-4_18 CrossRefGoogle Scholar
  10. 10.
    Ferme, V., Ivanchikj, A., Pautasso, C.: Estimating the cost for executing business processes in the cloud. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNBIP, vol. 260, pp. 72–88. Springer, Cham (2016). doi:10.1007/978-3-319-45468-9_5 CrossRefGoogle Scholar
  11. 11.
    Ferme, V., et al.: Workflow management systems benchmarking: unfulfilled expectations and lessons learned. In: Proceedings of ICSE 2017, May 2017Google Scholar
  12. 12.
    Gerth, C., et al.: Detection of semantically equivalent fragments for business process model change management. In: Proceedings of SCC, pp. 57–64. IEEE (2010)Google Scholar
  13. 13.
    Gounaris, A.: Towards automated performance optimization of BPMN business processes. In: Ivanović, M., et al. (eds.) ADBIS 2016. CCIS, vol. 637, pp. 19–28. Springer, Cham (2016). doi:10.1007/978-3-319-44066-8_2 CrossRefGoogle Scholar
  14. 14.
    Hamby, D.: A review of techniques for parameter sensitivity analysis of environmental models. Environ. Monit. Assess. 32(2), 135–154 (1994)CrossRefGoogle Scholar
  15. 15.
    Hoste, K., Eeckhout, L.: Cole: compiler optimization level exploration. In: Proceedings of CGO, pp. 165–174. ACM (2008)Google Scholar
  16. 16.
    Jarke, M., Koch, J.: Query optimization in database systems. ACM Comput. Surv. (CsUR) 16(2), 111–152 (1984)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Jordan, D., Evdemon, J.: Business Process Model And Notation (BPMN) Version 2.0. OMG. http://www.omg.org/spec/BPMN/2.0/
  18. 18.
    Koehler, J., Vanhatalo, J.: Process anti-patterns: how to avoid the common traps of business process modeling. IBM WebSph. Dev. Tech. J. 10(2), 4 (2007)Google Scholar
  19. 19.
    Marusteri, M., Bacarea, V.: Comparing groups for statistical differences: how to choose the right statistical test? Biochemia Medica 20(1), 15–32 (2010)CrossRefGoogle Scholar
  20. 20.
    Mendling, J.: Metrics for Process Models: Empirical Foundations of Verification, Error Prediction, and Guidelines for Correctness. LNBIP, vol. 6. Springer, Heidelberg (2008). doi:10.1007/978-3-540-89224-3 Google Scholar
  21. 21.
    Mendling, J., Reijers, H.A., van der Aalst, W.M.: Seven process modeling guidelines (7PMG). Inf. Softw. Technol. 52(2), 127–136 (2010)CrossRefGoogle Scholar
  22. 22.
    Muehlen, M., Recker, J.: How much language is enough? Theoretical and practical use of the business process modeling notation. In: Bellahsène, Z., Léonard, M. (eds.) CAiSE 2008. LNCS, vol. 5074, pp. 465–479. Springer, Heidelberg (2008). doi:10.1007/978-3-540-69534-9_35 CrossRefGoogle Scholar
  23. 23.
    Recker, J.: Empirical investigation of the usefulness of gateway constructs in process models. Eur. J. Inf. Syst. 22(6), 673–689 (2013)CrossRefGoogle Scholar
  24. 24.
    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
  25. 25.
    Rosa, M.L., et al.: Managing process model complexity via concrete syntax modifications. IEEE Trans. Ind. Inf. 7(2), 255–265 (2011)CrossRefGoogle Scholar
  26. 26.
    Sengupta, A., Pal, T.K.: On comparing interval numbers. Eur. J. Oper. Res. 127(1), 28–43 (2000)MathSciNetCrossRefMATHGoogle Scholar
  27. 27.
    Skouradaki, M., Ferme, V., Pautasso, C., Leymann, F., Hoorn, A.: Micro-Benchmarking BPMN 2.0 workflow management systems with workflow patterns. In: Nurcan, S., Soffer, P., Bajec, M., Eder, J. (eds.) CAiSE 2016. LNCS, vol. 9694, pp. 67–82. Springer, Cham (2016). doi:10.1007/978-3-319-39696-5_5 Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ana Ivanchikj
    • 1
  • Vincenzo Ferme
    • 1
  • Cesare Pautasso
    • 1
  1. 1.Software Institute, Faculty of InformaticsUSILuganoSwitzerland

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