Simulation of Multi-perspective Declarative Process Models

  • Lars AckermannEmail author
  • Stefan Schönig
  • Stefan Jablonski
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 281)


Flexible business processes can often be represented more easily using a declarative process modeling language (DPML) rather than an imperative language. Process mining techniques can be used to automate the discovery of process models. One way to evaluate process mining techniques is to synthesize event logs from a source model via simulation techniques and to compare the discovered model with the source model. Though there are several declarative process mining techniques, there is a lack of simulation approaches. Process models also involve multiple aspects, like the flow of activities and resource assignment constraints. The simulation approach at hand automatically synthesizes event logs that conform to a given model specified in the multi-perspective, declarative language DPIL. Our technique translates DPIL constraints to a logic language called Alloy. A formula-analysis step is the actual log generation. We evaluate our technique with a concise example and describe an alternative configuration to simulate event logs based on an assumed partial execution as well as on properties that are intended to be checked. We complement the quality evaluation by a performance analysis.


Simulation of business processes Predictive analytics Multi-perspective process mining 



The authors would like to thank Prof. Westfechtel, Felix Schwägerl (University of Bayreuth) and Prof. Daniel Jackson (MIT) for providing tips and literature about modeling and analysis with Alloy.


  1. 1.
    van der Aalst, W.M.P.: Business process simulation revisited. Enterp. Organ. Model. Simul. 63, 1–14 (2010)Google Scholar
  2. 2.
    Frank, U.: Multi-perspective enterprise modeling (memo) conceptual framework and modeling languages. In: HICSS, pp. 1258–1267 (2002)Google Scholar
  3. 3.
    Brown, A.L., Kane, M.J.: Preschool children can learn to transfer: learning to learn and learning from example. Cogn. Psychol. 20, 493–523 (1988)CrossRefGoogle Scholar
  4. 4.
    Schönig, S., Cabanillas, C., Jablonski, S., Mendling, J.: Mining the organisational perspective in agile business processes. In: BPMDS, pp. 37–52 (2015)Google Scholar
  5. 5.
    Di Ciccio, C., Bernardi, M.L., Cimitile, M., Maggi, F.M.: Generating event logs through the simulation of declare models. In: Barjis, J., Pergl, R., Babkin, E. (eds.) EOMAS 2015. LNBIP, vol. 231, pp. 20–36. Springer, Cham (2015). doi: 10.1007/978-3-319-24626-0_2 CrossRefGoogle Scholar
  6. 6.
    De Medeiros, A.A., Günther, C.W.: Process mining: using cpn tools to create test logs for mining algorithms. In: Proceedings of CPN, vol. 576 (2005)Google Scholar
  7. 7.
    Burattin, A., Sperduti, A.: PLG: a framework for the generation of business process models and their execution logs. In: Muehlen, M., Su, J. (eds.) BPM 2010. LNBIP, vol. 66, pp. 214–219. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-20511-8_20 CrossRefGoogle Scholar
  8. 8.
    Fahland, D., Lübke, D., Mendling, J., Reijers, H., Weber, B., Weidlich, M., Zugal, S.: Declarative versus imperative process modeling languages: the issue of understandability. In: Halpin, T., Krogstie, J., Nurcan, S., Proper, E., Schmidt, R., Soffer, P., Ukor, R. (eds.) BPMDS/EMMSAD -2009. LNBIP, vol. 29, pp. 353–366. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-01862-6_29 CrossRefGoogle Scholar
  9. 9.
    Pichler, P., Weber, B., Zugal, S., Pinggera, J., Mendling, J., Reijers, H.A.: Imperative versus declarative process modeling languages: an empirical investigation. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 383–394. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-28108-2_37 CrossRefGoogle Scholar
  10. 10.
    Zeising, M., Schönig, S., Jablonski, S.: Towards a common platform for the support of routine and agile business processes. In: CollaborateCom (2014)Google Scholar
  11. 11.
    Maggi, F.M., Bose, R.P.J.C., Aalst, W.M.P.: A knowledge-based integrated approach for discovering and repairing declare maps. In: Salinesi, C., Norrie, M.C., Pastor, Ó. (eds.) CAiSE 2013. LNCS, vol. 7908, pp. 433–448. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38709-8_28 CrossRefGoogle Scholar
  12. 12.
    Schönig, S., Rogge-Solti, A., Cabanillas, C., Jablonski, S., Mendling, J.: Efficient and customisable declarative process mining with SQL. In: Nurcan, S., Soffer, P., Bajec, M., Eder, J. (eds.) CAiSE 2016. LNCS, vol. 9694, pp. 290–305. Springer, Cham (2016). doi: 10.1007/978-3-319-39696-5_18 Google Scholar
  13. 13.
    Ackermann, L., Schönig, S., Jablonski, S.: Towards simulation- and mining-based translation of resource-aware process models. In: Proceedings of ReMa (2016)Google Scholar
  14. 14.
    Jackson, D.: Software Abstractions: Logic, Language, and Analysis. MIT Press, Cambridge (2012)Google Scholar
  15. 15.
    Montali, M., Chesani, F., Mello, P., Maggi, F.M.: Towards data-aware constraints in declare. In: Proceedings of the 28th SAC, pp. 1391–1396. ACM (2013)Google Scholar
  16. 16.
    Barba, I., Weber, B., Del Valle, C., Jiménez-Ramírez, A.: User recommendations for the optimized execution of business processes. DKE 86, 61–84 (2013)CrossRefGoogle Scholar
  17. 17.
    Warmer, J.B., Kleppe, A.G.: The Object Constraint Language: Precise Modeling With Uml (Addison-Wesley OTS). Addison-Wesley Professional, Boston (1998)Google Scholar
  18. 18.
    Zazkis, R., Chernoff, E.J.: What makes a counterexample exemplary? Educ. Stud. Math. 68(3), 195–208 (2008)CrossRefGoogle Scholar
  19. 19.
    Bussler, C.: Analysis of the organization modeling capability of workflow-management-systems. In: PRIISM 1996 Conference Proceedings, pp. 438–455 (1996)Google Scholar
  20. 20.
    Object Management Group (OMG): Business process model and notation (bpmn) version 2.0. Technical report, January 2011Google Scholar
  21. 21.
    van der Aalst, W.: Process Mining: Discovery, Conformance and Enhancement of Business Processes, vol. 2. Springer, New York (2011)CrossRefzbMATHGoogle Scholar
  22. 22.
    Verbeek, H.M.W., Buijs, J.C.A.M., Dongen, B.F., Aalst, W.M.P.: XES, XESame, and ProM 6. In: Soffer, P., Proper, E. (eds.) CAiSE Forum 2010. LNBIP, vol. 72, pp. 60–75. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-17722-4_5 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Lars Ackermann
    • 1
    Email author
  • Stefan Schönig
    • 1
  • Stefan Jablonski
    • 1
  1. 1.University of BayreuthBayreuthGermany

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