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Aligning Modeled and Observed Behavior: A Compromise Between Computation Complexity and Quality

  • Boudewijn van Dongen
  • Josep Carmona
  • Thomas Chatain
  • Farbod Taymouri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10253)

Abstract

Certifying that a process model is aligned with the real process executions is perhaps the most desired feature a process model may have: aligned process models are crucial for organizations, since strategic decisions can be made easier on models instead of on plain data. In spite of its importance, the current algorithmic support for computing alignments is limited: either techniques that explicitly explore the model behavior (which may be worst-case exponential with respect to the model size), or heuristic approaches that cannot guarantee a solution, are the only alternatives. In this paper we propose a solution that sits right in the middle in the complexity spectrum of alignment techniques; it can always guarantee a solution, whose quality depends on the exploration depth used and local decisions taken at each step. We use linear algebraic techniques in combination with an iterative search which focuses on progressing towards a solution. The experiments show a clear reduction in the time required for reaching a solution, without sacrificing significantly the quality of the alignment obtained.

Keywords

Process mining Conformance checking ILP Heuristics Alignments 

References

  1. 1.
    van der Aalst, W.M.P.: Process Mining - Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)zbMATHGoogle Scholar
  2. 2.
    Adriansyah, A.: Aligning observed and modeled behavior. Ph.D. thesis, Technische Universiteit Eindhoven (2014)Google Scholar
  3. 3.
    Adriansyah, A., Munoz-Gama, J., Carmona, J., van Dongen, B.F., van der Aalst, W.M.P.: Measuring precision of modeled behavior. Inf. Syst. E-Bus. Manag. 13(1), 37–67 (2015)CrossRefGoogle Scholar
  4. 4.
    Taymouri, F., Carmona, J.: A recursive paradigm for aligning observed behavior of large structured process models. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 197–214. Springer, Cham (2016). doi: 10.1007/978-3-319-45348-4_12 CrossRefGoogle Scholar
  5. 5.
    van der Aalst, W.M.P.: Decomposing Petri nets for process mining: a generic approach. Distrib. Parallel Databases 31(4), 471–507 (2013)CrossRefGoogle Scholar
  6. 6.
    Munoz-Gama, J., Carmona, J., van der Aalst, W.M.P.: Single-entry single-exit decomposed conformance checking. Inf. Syst. 46, 102–122 (2014)CrossRefGoogle Scholar
  7. 7.
    Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P.: Balanced multi-perspective checking of process conformance. Computing 98(4), 407–437 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    de Leoni, M., Maggi, F.M., van der Aalst, W.M.P.: An alignment-based framework to check the conformance of declarative process models and to preprocess event-log data. Inf. Syst. 47, 258–277 (2015)CrossRefGoogle Scholar
  9. 9.
    Burattin, A., Maggi, F.M., Sperduti, A.: Conformance checking based on multi-perspective declarative process models. Expert Syst. Appl. 65, 194–211 (2016)CrossRefGoogle Scholar
  10. 10.
    Lu, X., Mans, R., Fahland, D., van der Aalst, W.M.P.: Conformance checking in healthcare based on partially ordered event data. In: Proceedings of the 2014 IEEE Emerging Technology and Factory Automation, ETFA 2014, Barcelona, Spain, 16–19 September 2014, pp. 1–8 (2014)Google Scholar
  11. 11.
    Lu, X., Fahland, D., van der Aalst, W.M.P.: Conformance checking based on partially ordered event data. In: Fournier, F., Mendling, J. (eds.) BPM 2014. LNBIP, vol. 202, pp. 75–88. Springer, Cham (2015). doi: 10.1007/978-3-319-15895-2_7 Google Scholar
  12. 12.
    Murata, T.: Petri nets: properties, analysis and applications. Proc. IEEE 77(4), 541–574 (1989)CrossRefGoogle Scholar
  13. 13.
    Silva, M., Terue, E., Colom, J.M.: Linear algebraic and linear programming techniques for the analysis of place/transition net systems. In: Reisig, W., Rozenberg, G. (eds.) ACPN 1996. LNCS, vol. 1491, pp. 309–373. Springer, Heidelberg (1998). doi: 10.1007/3-540-65306-6_19 CrossRefGoogle Scholar
  14. 14.
    Mannhardt, F.: Sepsis Cases - Event Log. Eindhoven University of Technology. Dataset (2016). http://dx.doi.org/10.4121/uuid:915d2bfb-7e84-49ad-a286-dc35f063a460
  15. 15.
    Gurobi Optimization, I.: Gurobi optimizer reference manual (2016)Google Scholar
  16. 16.
    Berkelaar, M., Eikland, K., Notebaert, P.: lpsolve : Open source (Mixed-Integer) Linear Programming systemGoogle Scholar
  17. 17.
    Munoz-Gama, J., Carmona, J., van der Aalst, W.M.P.: Conformance checking in the large: partitioning and topology. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 130–145. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40176-3_11 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Boudewijn van Dongen
    • 1
  • Josep Carmona
    • 2
  • Thomas Chatain
    • 3
  • Farbod Taymouri
    • 2
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Universitat Politècnica de CatalunyaBarcelonaSpain
  3. 3.LSV, ENS Cachan, CNRS, Inria, Université Paris-SaclayCachanFrance

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