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Domain-Driven Reduction Optimization of Recovered Business Processes

  • Alex Tomasi
  • Alessandro Marchetto
  • Chiara Di Francescomarino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7515)

Abstract

Process models play a key role in taking decisions when existing procedures and systems need to be changed and improved. However, these models are often not available or not aligned with the actual process implementation. In these cases, process model recovery techniques can be applied to analyze the existing system implementation and capture the underlying business process models. Several techniques have been proposed in the literature to recover business processes, although the resulting processes are often complex, intricate and thus difficult to understand for business analysts.

In this paper, we propose a process reduction technique based on multi-objective optimization, which minimizes at the same time process complexity, non-conformances, and loss of business content. This allows us to improve the process model understandability by decreasing its structural complexity, while preserving the completeness of the described business and domain-specific information. We conducted a case study based on a real-life e-commerce system. Results indicate that by balancing complexity, conformance and business content our technique produces understandable and meaningful reduced process models.

Keywords

Business Process Recovery Multi-Objective Optimization and Ontology 

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References

  1. 1.
    van der Aalst, W., Weijter, A., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering 16, 2004 (2003)Google Scholar
  2. 2.
    Alves de Medeiros, A., Weijters, A., van der Aalst, W.: Genetic process mining: An experimental evaluation. Journal of Data Mining and Knowledge Discovery 14(2), 245–304 (2006)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bose, R., van der Aalst, W.: Context aware trace clustering: Towards improving process mining results. In: Proc. of Symp. on Discrete Algorithms (SDM-SIAM), pp. 401–412 (2009)Google Scholar
  4. 4.
    Cardoso, J., Mendling, J., Neumann, G., Reijers, H.: A discourse on complexity of process models. In: Proc. of Workshop on Business Process Intelligence (BPI), pp. 115–126 (2006)Google Scholar
  5. 5.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  6. 6.
    Di Francescomarino, C., Marchetto, A., Tonella, P.: Cluster-based modularization of processes recovered from web applications. Journal of Software Maintenance and Evolution: Research and Practice (2010), doi: 10.1002/smr.518Google Scholar
  7. 7.
    Di Francescomarino, C., Ghidini, C., Rospocher, M., Serafini, L., Tonella, P.: Reasoning on Semantically Annotated Processes. In: Bouguettaya, A., Krueger, I., Margaria, T. (eds.) ICSOC 2008. LNCS, vol. 5364, pp. 132–146. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Di Francescomarino, C., Tonella, P.: Supporting Ontology-Based Semantic Annotation of Business Processes with Automated Suggestions. In: Halpin, T., Krogstie, J., Nurcan, S., Proper, E., Schmidt, R., Soffer, P., Ukor, R. (eds.) BPMDS 2009 and EMMSAD 2009. LNBIP, vol. 29, pp. 211–223. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Marchetto, A., Di Francescomarino, C., Tonella, P.: Optimizing the Trade-Off between Complexity and Conformance in Process Reduction. In: Cohen, M.B., Ó Cinnéide, M. (eds.) SSBSE 2011. LNCS, vol. 6956, pp. 158–172. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Reijers, H., Mendling, J.: Modularity in Process Models: Review and Effects. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 20–35. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Rozinat, A., van der Aalst, W.: Conformance checking of processes based on monitoring real behavior. Information Systems 33(1), 64–95 (2008)CrossRefGoogle Scholar
  12. 12.
    Thomas, O., Fellmann, M.: Semantic epc: Enhancing process modeling using ontology languages. In: SBPM. CEUR Workshop Proceedings, vol. 251. CEUR-WS.org (2007)Google Scholar
  13. 13.
    Tonella, P., Marchetto, A., Nguyen, C., Jia, Y., Lakhotia, K., Harman, M.: Finding the optimal balance between over and under approximation of models inferred from execution logs. In: Int. Conference on Software Testing, Verification and Validation (ICST), pp. 21–30 (2012)Google Scholar
  14. 14.
    van der Aalst, W., van Dongen, B., Herbst, J., Maruster, L.G., Schimm, W.A.: Workflow mining: A survey of issues and approaches. Journal of Data and Knowledge Engineering 47(2), 237–267 (2003)CrossRefGoogle Scholar
  15. 15.
    Veiga, G.M., Ferreira, D.R.: Understanding spaghetti models with sequence clustering for prom. In: Proc. of Workshop on Business Process Intelligence (BPI), Ulm, Germany (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alex Tomasi
    • 1
  • Alessandro Marchetto
    • 1
  • Chiara Di Francescomarino
    • 1
  1. 1.FBK-CITTrentoItaly

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