Advertisement

Finding Complex Process-Structures by Exploiting the Token-Game

  • Lisa Luise MannelEmail author
  • Wil M. P. van der Aalst
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11522)

Abstract

In process discovery, the goal is to find, for a given event log, the model describing the underlying process. While process models can be represented in a variety of ways, in this paper we focus on the representation by Petri nets. Using an approach inspired by language-based regions, we start with a Petri net without any places, and then insert the maximal set of places considered fitting with respect to the behavior described by the log. Traversing and evaluating the whole set of all possible places is not feasible since their number is exponential in the number of activities. Therefore, we propose a strategy to drastically prune this search space to a small number of candidates, while still ensuring that all fitting places are found. This allows us to derive complex model structures that other discovery algorithms fail to discover. In contrast to traditional region-based approaches this new technique can handle infrequent behavior and therefore also noisy real-life event data. The drastic decrease of computation time achieved by our pruning strategy, as well as our noise handling capability, is demonstrated and evaluated by performing various experiments.

Keywords

Process discovery Petri nets Language-based regions 

Notes

Acknowledgments

We thank the Alexander von Humboldt (AvH) Stiftung for supporting our research. We thank Alessandro Berti for his support with implementing the eST-Miner, and Sebastiaan J. van Zelst for reviewing this paper.

References

  1. 1.
    van der Aalst, W.: Process Mining: Data Science in Action, 2nd edn. Springer, Heidelberg (2016).  https://doi.org/10.1007/978-3-662-49851-4CrossRefGoogle Scholar
  2. 2.
    Bergenthum, R., Desel, J., Lorenz, R., Mauser, S.: Process mining based on regions of languages. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 375–383. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-75183-0_27CrossRefGoogle Scholar
  3. 3.
    van der Aalst, W.: Discovering the “glue” connecting activities - exploiting monotonicity to learn places faster. In: It’s All About Coordination - Essays to Celebrate the Lifelong Scientific Achievements of Farhad Arbab, pp. 1–20 (2018)Google Scholar
  4. 4.
    Wen, L., van der Aalst, W., Wang, J., Sun, J.: Mining process models with non-free-choice constructs. Data Min. Knowl. Discov. 15(2), 145–180 (2007)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs - a constructive approach. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 311–329. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-38697-8_17CrossRefGoogle Scholar
  6. 6.
    Weijters, A., van der Aalst, W.: Rediscovering workflow models from event-based data using little thumb. Integr. Comput.-Aided Eng. 10(2), 151–162 (2003)CrossRefGoogle Scholar
  7. 7.
    Badouel, E., Bernardinello, L., Darondeau, P.: Petri Net Synthesis. TTCSAES. Springer, Heidelberg (2015).  https://doi.org/10.1007/978-3-662-47967-4CrossRefzbMATHGoogle Scholar
  8. 8.
    Ehrenfeucht, A., Rozenberg, G.: Partial (set) 2-structures. Acta Informatica 27(4), 343–368 (1990)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Carmona, J., Cortadella, J., Kishinevsky, M.: A region-based algorithm for discovering petri nets from event logs. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 358–373. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-85758-7_26CrossRefGoogle Scholar
  10. 10.
    van der Aalst, W., Rubin, V., Verbeek, H., van Dongen, B., Kindler, E., Günther, C.W.: Process mining: a two-step approach to balance between underfitting and overfitting. Softw. Syst. Model. 9(1), 87 (2008)CrossRefGoogle Scholar
  11. 11.
    Lorenz, R., Mauser, S., Juhás, G.: How to synthesize nets from languages: A survey. In: Proceedings of the 39th Conference on Winter Simulation: 40 Years! The Best is Yet to Come, WSC 2007, pp. 637–647. IEEE Press, Piscataway (2007)Google Scholar
  12. 12.
    Darondeau, P.: Deriving unbounded Petri nets from formal languages. In: Sangiorgi, D., de Simone, R. (eds.) CONCUR 1998. LNCS, vol. 1466, pp. 533–548. Springer, Heidelberg (1998).  https://doi.org/10.1007/BFb0055646CrossRefGoogle Scholar
  13. 13.
    Bergenthum, R., Desel, J., Lorenz, R., Mauser, S.: Synthesis of Petri nets from finite partial languages. Fundam. Inf. 88(4), 437–468 (2008)MathSciNetzbMATHGoogle Scholar
  14. 14.
    van der Werf, J.M.E.M., van Dongen, B.F., Hurkens, C.A.J., Serebrenik, A.: Process discovery using integer linear programming. In: van Hee, K.M., Valk, R. (eds.) PETRI NETS 2008. LNCS, vol. 5062, pp. 368–387. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-68746-7_24CrossRefGoogle Scholar
  15. 15.
    van Zelst, S.J., van Dongen, B.F., van der Aalst, W.M.P.: Avoiding over-fitting in ILP-based process discovery. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 163–171. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-23063-4_10CrossRefGoogle Scholar
  16. 16.
    van Zelst, S., van Dongen, B., van der Aalst, W.: ILP-based process discovery using hybrid regions. In: ATAED@Petri Nets/ACSD (2015)Google Scholar
  17. 17.
    Berthelot, G.: Checking properties of nets using transformation. In: Rozenberg, G. (ed.) APN 1985. LNCS, vol. 222, pp. 19–40. Springer, Berlin (1986).  https://doi.org/10.1007/BFb0016204CrossRefGoogle Scholar
  18. 18.
    Berthelot, G.: Transformations and decompositions of nets. In: Brauer, W., Reisig, W., Rozenberg, G. (eds.) ACPN 1986. LNCS, vol. 254, pp. 359–376. Springer, Heidelberg (1987).  https://doi.org/10.1007/978-3-540-47919-2_13CrossRefGoogle Scholar
  19. 19.
    Colom, J.M., Silva, M.: Improving the linearly based characterization of P/T nets. In: Rozenberg, G. (ed.) ICATPN 1989. LNCS, vol. 483, pp. 113–145. Springer, Heidelberg (1991).  https://doi.org/10.1007/3-540-53863-1_23CrossRefGoogle Scholar
  20. 20.
    Garcia-Valles, F., Colom, J.: Implicit places in net systems. In: Proceedings 8th International Workshop on Petri Nets and Performance Models, pp. 104–113 (1999)Google Scholar
  21. 21.
    Berthomieu, B., Botlan, D.L., Dal-Zilio, S.: Petri net reductions for counting markings. CoRR abs/1807.02973 (2018)Google Scholar
  22. 22.
    van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: The ProM framework: a new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005).  https://doi.org/10.1007/11494744_25CrossRefGoogle Scholar
  23. 23.
    Polato, M.: Dataset belonging to the help desk log of an Italian company (2017)Google Scholar
  24. 24.
    Mannhardt, F.: Sepsis cases - event log (2016)Google Scholar
  25. 25.
    De Leoni, M., Mannhardt, F.: Road traffic fine management process (2015)Google Scholar
  26. 26.
    van der Aalst, W.M.P.: Event Logs and Models Used in Process Mining: Data Science in Action (2016). http://www.processmining.org/event_logs_and_models_used_in_book
  27. 27.
    Lu, X., Fahland, D., van den Biggelaar, F.J.H.M., van der Aalst, W.M.P.: Handling duplicated tasks in process discovery by refining event labels. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 90–107. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-45348-4_6CrossRefGoogle Scholar
  28. 28.
    Li, J., Liu, D., Yang, B.: Process mining: extending \({\alpha }\)-algorithm to mine duplicate tasks in process logs. In: Chang, K.C.-C., et al. (eds.) APWeb/WAIM -2007. LNCS, vol. 4537, pp. 396–407. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-72909-9_43CrossRefGoogle Scholar
  29. 29.
    Wen, L., Wang, J., Sun, J.: Mining invisible tasks from event logs. In: Dong, G., Lin, X., Wang, W., Yang, Y., Yu, J.X. (eds.) APWeb/WAIM -2007. LNCS, vol. 4505, pp. 358–365. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-72524-4_38CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lisa Luise Mannel
    • 1
    Email author
  • Wil M. P. van der Aalst
    • 1
  1. 1.Process and Data Science (PADS)RWTH Aachen UniversityAachenGermany

Personalised recommendations