Constraint-Based Composition of Business Process Models

  • Piotr WiśniewskiEmail author
  • Krzysztof Kluza
  • Mateusz Ślażyński
  • Antoni Ligęza
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 308)


Process models help organizations to visualize and optimize their activities, and achieve their business goals in a more efficient way. Modeling a business process requires exact information about possible execution sequences of the activities as well as process modeling notation knowledge. We present a method of business process composition based on the constraint programming technique. Taking task specifications as the input, our solution can generate a workflow log which can be used to discover the model using any process mining technique.


Business processes BPMN Automated planning Constraint programming Process mining Business process composition 


  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.
    Ghattas, J., Soffer, P., Peleg, M.: Improving business process decision making based on past experience. Decis. Support Syst. 59, 93–107 (2014)CrossRefGoogle Scholar
  3. 3.
    Heinrich, B., Schön, D.: Automated planning of process models: the construction of simple merges. In: European Conference on Information Systems (ECIS) (2016)Google Scholar
  4. 4.
    Heinrich, B., Schön, D.: Automated planning of context-aware process models. In: Becker, J., vom Brocke, J., de Marco, M. (eds.) ECIS (2015)Google Scholar
  5. 5.
    Havur, G., Cabanillas, C., Mendling, J., Polleres, A.: Automated resource allocation in business processes with answer set programming. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 191–203. Springer, Cham (2016). CrossRefGoogle Scholar
  6. 6.
    De Giacomo, G., Maggi, F., Marella, A., Sardina, S.: Computing trace alignment against declarative process models through planning. In: Proceedings of the International Conference on Automated Planning and Scheduling, pp. 367–375 (2016)Google Scholar
  7. 7.
    Barba Rodriguez, I.: Constraint-based planning and scheduling techniques for the optimized management of business processes. Ph.d. Universidad de Sevilla (2011)Google Scholar
  8. 8.
    Schneeweis, D.: Constraint-based scheduling and planing in medical facilities with BPMN. In: The 19th International Conference on Principles and Practice of Constraint Programming. Doctoral Program, pp. 115–120 (2013)Google Scholar
  9. 9.
    Meyer, H., Weske, M.: Automated service composition using heuristic search. In: Dustdar, S., Fiadeiro, J.L., Sheth, A.P. (eds.) BPM 2006. LNCS, vol. 4102, pp. 81–96. Springer, Heidelberg (2006). CrossRefGoogle Scholar
  10. 10.
    Graml, T., Bracht, R., Spies, M.: Patterns of business rules to enable agile business processes. In: 11th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2007), pp. 365–365 (2007)Google Scholar
  11. 11.
    Kumar, A., Shan, Z.: Algorithms based on pattern analysis for verification and adapter creation for business process composition. In: Meersman, R., Tari, Z. (eds.) OTM 2008. LNCS, vol. 5331, pp. 120–138. Springer, Heidelberg (2008). CrossRefGoogle Scholar
  12. 12.
    Skouradaki, M., Andrikopoulos, V., Leymann, F.: Representative BPMN 2.0 process models generation from recurring structures. In: Proceedings of the 23rd IEEE International Conference on Web Services, pp. 468–475. IEEE, June 2016Google Scholar
  13. 13.
    Kluza, K., Wiśniewski, P.: Spreadsheet-based business process modeling. In: 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1355–1358. IEEE (2016)Google Scholar
  14. 14.
    Nethercote, N., Stuckey, P.J., Becket, R., Brand, S., Duck, G.J., Tack, G.: MiniZinc: towards a standard CP modelling language. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 529–543. Springer, Heidelberg (2007). CrossRefGoogle Scholar
  15. 15.
    Schulte, C., Stuckey, P.J.: Efficient constraint propagation engines. ACM Trans. Program. Lang. Syst. 31(1), 2:1–2:43 (2008)CrossRefGoogle Scholar
  16. 16.
    van der Aalst, W.M., van Dongen, B.F., Günther, C.W., Rozinat, A., Verbeek, E., Weijters, T.: ProM: the process mining toolkit. BPM (Demos) 489(31), 2 (2009)Google Scholar
  17. 17.
    van Beest, N.R., Russell, N., ter Hofstede, A.H., Lazovik, A.: Achieving intention-centric BPM through automated planning. In: 2014 IEEE 7th International Conference on Service-Oriented Computing and Applications, pp. 191–198 (2014)Google Scholar
  18. 18.
    Qin, J., Fahringer, T., Prodan, R.: A novel graph based approach for automatic composition of high quality grid workflows. In: Proceedings of the 18th ACM International Symposium on High Performance Distributed Computing, HPDC 2009, pp. 167–176. ACM (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Piotr Wiśniewski
    • 1
    Email author
  • Krzysztof Kluza
    • 1
  • Mateusz Ślażyński
    • 1
  • Antoni Ligęza
    • 1
  1. 1.AGH University of Science and TechnologyKrakowPoland

Personalised recommendations