Discovering Reference Models by Mining Process Variants Using a Heuristic Approach

  • Chen Li
  • Manfred Reichert
  • Andreas Wombacher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5701)


Recently, a new generation of adaptive Process-Aware Information Systems (PAISs) has emerged, which enables structural process changes during runtime. Such flexibility, in turn, leads to a large number of process variants derived from the same model, but differing in structure. Generally, such variants are expensive to configure and maintain. This paper provides a heuristic search algorithm which fosters learning from past process changes by mining process variants. The algorithm discovers a reference model based on which the need for future process configuration and adaptation can be reduced. It additionally provides the flexibility to control the process evolution procedure, i.e., we can control to what degree the discovered reference model differs from the original one. As benefit, we cannot only control the effort for updating the reference model, but also gain the flexibility to perform only the most important adaptations of the current reference model. Our mining algorithm is implemented and evaluated by a simulation using more than 7000 process models. Simulation results indicate strong performance and scalability of our algorithm even when facing large-sized process models.


Process Variant Reference Model Candidate Model Change Operation Order Matrix 
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.
    Bae, J., Liu, L., Caverlee, J., Rouse, W.B.: Process mining, discovery, and integration using distance measures. In: ICWS 2006, pp. 479–488 (2006)Google Scholar
  2. 2.
    Alves de Medeiros, A.K.: Genetic Process Mining. PhD thesis, Eindhoven University of Technology, NL (2006)Google Scholar
  3. 3.
    Günther, C.W., Rinderle-Ma, S., Reichert, M., van der Aalst, W.M.P., Recker, J.: Using process mining to learn from process changes in evolutionary systems. Int’l Journal of Business Process Integration and Management 3(1), 61–78 (2008)CrossRefGoogle Scholar
  4. 4.
    Hallerbach, A., Bauer, T., Reichert, M.: Managing process variants in the process lifecycle. In: Proc. 10th Int’l Conf. on Enterprise Information Systems (ICEIS 2008), pp. 154–161 (2008)Google Scholar
  5. 5.
    Li, C., Reichert, M., Wombacher, A.: Discovering reference process models by mining process variants. In: ICWS 2008, pp. 45–53. IEEE Computer Society Press, Los Alamitos (2008)Google Scholar
  6. 6.
    Li, C., Reichert, M., Wombacher, A.: Mining process variants: Goals and issues. In: IEEE SCC (2), pp. 573–576. IEEE Computer Society Press, Los Alamitos (2008)Google Scholar
  7. 7.
    Li, C., Reichert, M., Wombacher, A.: On measuring process model similarity based on high-level change operations. In: Li, Q., Spaccapietra, S., Yu, E., Olivé, A. (eds.) ER 2008. LNCS, vol. 5231, pp. 248–264. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Li, C., Reichert, M., Wombacher, A.: A heuristic approach for discovering reference models by mining process model variants. Technical Report TR-CTIT-09-08, University of Twente, NL (2009)Google Scholar
  9. 9.
    Li, C., Reichert, M., Wombacher, A.: What are the problem makers: Ranking activities according to their relevance for process changes. In: ICWS 2009. IEEE Computer Society Press, Los Alamitos (to appear, 2009)Google Scholar
  10. 10.
    Luger, G.F.: Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Pearson Education, London (2005)Google Scholar
  11. 11.
    Reichert, M., Dadam, P.: ADEPTflex -supporting dynamic changes of workflows without losing control. J. of Intelligent Information Sys. 10(2), 93–129 (1998)CrossRefGoogle Scholar
  12. 12.
    Reichert, M., Rinderle, S., Kreher, U., Dadam, P.: Adaptive process management with ADEPT2. In: ICDE 2005, pp. 1113–1114. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  13. 13.
    Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. CRC Press, Boca Raton (2004)zbMATHGoogle Scholar
  14. 14.
    Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley, Reading (2005)Google Scholar
  15. 15.
    van der Aalst, W.M.P., Basten, T.: Inheritance of workflows: an approach to tackling problems related to change. Theor. Comput. Sci. 270(1-2), 125–203 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    van der Aalst, W.M.P., Weijters, T., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE TKDE 16(9), 1128–1142 (2004)Google Scholar
  17. 17.
    Weber, B., Reichert, M., Rinderle-Ma, S.: Change patterns and change support features - enhancing flexibility in process-aware information systems. Data and Knowledge Engineering 66(3), 438–466 (2008)CrossRefGoogle Scholar
  18. 18.
    Weber, B., Reichert, M., Wild, W., Rinderle-Ma, S.: Providing integrated life cycle support in process-aware information systems. Int’l Journal of Cooperative Information Systems (IJCIS), 19(1) (2009)Google Scholar
  19. 19.
    Weijters, A.J.M.M., van der Aalst, W.M.P.: Rediscovering workflow models from event-based data using little thumb. Integr. Com.-Aided Eng. 10(2), 151–162 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Chen Li
    • 1
  • Manfred Reichert
    • 2
  • Andreas Wombacher
    • 1
  1. 1.Computer Science DepartmentUniversity of TwenteThe Netherlands
  2. 2.Institute of Databases and Information SystemsUlm UniversityGermany

Personalised recommendations