Extending BPM Environments of Your Choice with Performance Related Decision Support

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5701)


What-if Simulations have been identified as one solution for business performance related decision support. Such support is especially useful in cases where it can be automatically generated out of Business Process Management (BPM) Environments from the existing business process models and performance parameters monitored from the executed business process instances. Currently, some of the available BPM Environments offer basic-level performance prediction capabilities. However, these functionalities are normally too limited to be generally useful for performance related decision support at business process level. In this paper, an approach is presented which allows the non-intrusive integration of sophisticated tooling for what-if simulations, analytic performance prediction tools, process optimizations or a combination of such solutions into already existing BPM environments. The approach abstracts from process modelling techniques which enable automatic decision support spanning processes across numerous BPM Environments. For instance, this enables end-to-end decision support for composite processes modelled with the Business Process Modelling Notation (BPMN) on top of existing Enterprise Resource Planning (ERP) processes modelled with proprietary languages.


Decision Support Business Process Business Process Management Business Process Modelling Notation Performance Analysis Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Snabe, J.H., Rosenber, A., Molle, C., Scavillo, M.: Business Process Management: The SAP Roadmap (2008)Google Scholar
  2. 2.
    JCOM (2008),
  3. 3.
    Associates, B.S.: The BPMS Report: EMC Documentum Process Suite 6.0 (2007)Google Scholar
  4. 4.
  5. 5.
    Fritzsche, M., Gilani, W., Fritzsche, C., Spence, I.T.A., Kilpatrick, P., Brown, J.: Towards utilizing model-driven engineering of composite applications for business performance analysis. In: Schieferdecker, I., Hartman, A. (eds.) ECMDA-FA 2008. LNCS, vol. 5095, pp. 369–380. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Harmon, P., Wolf, C.: The state of business process management (2008)Google Scholar
  7. 7.
    Banks, J., Carson, J.S., Nelson, B., Nicol, D.: Discrete-Event Simulation. Prentice-Hall, Englewood Cliffs (2005)Google Scholar
  8. 8.
    Rozinat, A., Wynn, M.T., van der Aalst, W.M.P., ter Hofstede, A.H.M., Fidge, C.J.: Workflow Simulation for Operational Decision Support Using Design, Historic and State Information. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 196–211. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Balsamo, S., Marzolla, M.: A simulation-based approach to software performance modeling. In: ESEC/FSE-11th. ACM, New York (2003)Google Scholar
  10. 10.
    D’Ambrogio, A.: A model transformation framework for the automated building of performance models from uml models. In: WOSP 2005. ACM Press, New York (2005)Google Scholar
  11. 11.
    Bertolino, A., Marchetti, E., Mirandola, R.: Real-time UML-based performance engineering to aid manager’s decisions in multi-project planning. In: WOSP 2002. ACM Press, New York (2002)Google Scholar
  12. 12.
    Franks, R.G.: DISSERTATION: Performance Analysis of Distributed Server Systems (1999)Google Scholar
  13. 13.
    Woodside, C.M., Neilson, J.E., Petriu, D.C., Majumdar, S.: The Stochastic Rendezvous Network Model for Performance of Synchronous Client-Server-like Distributed Software. IEEE, Los Alamitos (1995)zbMATHGoogle Scholar
  14. 14.
    XJ Technologies: AnyLogic — multi-paradigm simulation software,
  15. 15.
    Fritzsche, M., Johannes, J.: Putting Performance Engineering into Model-Driven Engineering: Model-Driven Performance Engineering. In: Giese, H. (ed.) MODELS 2008. LNCS, vol. 5002, pp. 164–175. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Fritzsche, M., Gilani, W., Spence, I., Brown, T.J., Kilpatrick, P., Bashroush, R.: Towards performance related decision support for model driven engineering of enterprise soa applications. In: 15th ECBS 2008, vol. 0. IEEE, Los Alamitos (2008)Google Scholar
  17. 17.
    Knöpfel, A., Gröne, B., Tabeling, P.: Fundamental Modeling Concepts: Effective Communication of IT Systems. John Wiley & Sons, Chichester (2006)Google Scholar
  18. 18.
    Petriu, D.B., Woodside, M.: An intermediate metamodel with scenarios and resources for generating performance models from UML designs. Software and Systems Modeling 6(2) (2007)Google Scholar
  19. 19.
    Rozinat, A., Wynn, M., Aalst, W., Hofstede, A., Fidge, C.: Workflow Simulation for Operational Decision Support Using Design, Historic and State Information. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 196–211. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  20. 20.
    Fritzsche, M., Jouault, F., Lämmel, R., Gilani, W.: Model Transformation Chains to integrated Performance related Decision Support into BPM Tool Chains. In: Invited submission fro the post- proceedings of the GTTSE 2009. LNCS. Springer, Heidelberg (2009)Google Scholar
  21. 21.
    Fritzsche, M., Johannes, J., Zschaler, S., Zherebtsov, A., Terekhov, A.: Application of tracing techniques in model-driven performance engineering. In: 4th ECMDA Traceability Workshop, ECMDA-TW (2008)Google Scholar
  22. 22.
    Bézivin, J., Jouault, F., Rosenthal, P., Valduriez, P.: Modeling in the Large and Modeling in the Small. In: Aßmann, U., Aksit, M., Rensink, A. (eds.) MDAFA 2003. LNCS, vol. 3599, pp. 33–46. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  23. 23.
    Barbero, F., Jouault, J.: Model Driven Management of Complex Systems: Implementing the Macroscope’s Vision. In: 15th ECBS 2008. IEEE, Los Alamitos (2008)Google Scholar
  24. 24.
    Fritzsche, M., Johannes, J., et al.: Systematic usage of embedded modelling languages in model transformation chains. In: SLE 2008. LNCS, vol. 5701. Springer, Heidelberg (2009)Google Scholar
  25. 25.
    Mehr, F., Schreier, U.: Modelling of Message Security Concerns with UML. In: 9th ICEIS (2007)Google Scholar
  26. 26.
    Fabro, M.D.D., Bézivin, J., Valduriez, P.: Weaving Models with the Eclipse AMW plugin. In: Eclipse Modeling Symposium, Eclipse Summit Europe (2006)Google Scholar
  27. 27.
    Vara1, J.M., Castro1, M.V.D., Fabro, M.D.D., Marcos, E.: Using Weaving Models to automate Model-Driven Web Engineering proposals. In: ZOCO 2008/ JISBD (2008)Google Scholar
  28. 28.
    Voelter, M., Groher, I., Kolb, B.: Mechanisms for Expressing Variability in Models and MDD Tool Chains. In: MDSD in Embedded Systems (2007)Google Scholar
  29. 29.
    Eclipse, C.: Eclipse graphical editing framework (gef) – version 3.4.2 (2009),
  30. 30.
    Woodside, M., Petriu, D.C., Petriu, D.B., Shen, H., Israr, T., Merseguer, J.: Performance by unified model analysis (PUMA). In: WOSP 2005. ACM Press, New York (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.SAP Research CEC BelfastUnited Kingdom
  2. 2.SAP Product & Technology Unit Suite CoreGermany
  3. 3.Queen’s University BelfastUnited Kingdom

Personalised recommendations