Advertisement

Efficient Discovery of Compact Maximal Behavioral Patterns from Event Logs

  • Mehdi AcheliEmail author
  • Daniela Grigori
  • Matthias Weidlich
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11483)

Abstract

Techniques for process discovery support the analysis of information systems by constructing process models from event logs that are recorded during system execution. In recent years, various algorithms to discover end-to-end process models have been proposed. Yet, they do not cater for domains in which process execution is highly flexible, as the unstructuredness of the resulting models renders them meaningless. It has therefore been suggested to derive insights about flexible processes by mining behavioral patterns, i.e., models of frequently recurring episodes of a process’ behavior. However, existing algorithms to mine such patterns suffer from imprecision and redundancy of the mined patterns and a comparatively high computational effort. In this work, we overcome these limitations with a novel algorithm, coined COBPAM (COmbination based Behavioral Pattern Mining). It exploits a partial order on potential patterns to discover only those that are compact and maximal, i.e. least redundant. Moreover, COBPAM exploits that complex patterns can be characterized as combinations of simpler patterns, which enables pruning of the pattern search space. Efficiency is improved further by evaluating potential patterns solely on parts of an event log. Experiments with real-world data demonstrates how COBPAM improves over the state-of-the-art in behavioral pattern mining.

Keywords

Behavioral patterns Process discovery Pattern mining 

References

  1. 1.
    Van der Aalst, W.: Process Mining: Data Science in Action. Springer, Berlin (2016).  https://doi.org/10.1007/978-3-662-49851-4CrossRefGoogle Scholar
  2. 2.
    Adriansyah, A.: Aligning observed and modeled behavior. Ph.D. thesis (2014)Google Scholar
  3. 3.
    Augusto, A., et al.: Automated Discovery of Process Models from Event Logs: Review and Benchmark (2018)Google Scholar
  4. 4.
    Bose, R.P.J.C., van der Aalst, W.M.: Context aware trace clustering: towards improving process mining results. In: 2009 SIAM International Conference on Data Mining (2009)Google Scholar
  5. 5.
    Bose, R.P.J.C., van der Aalst, W.M.P.: Trace clustering based on conserved patterns: towards achieving better process models. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 170–181. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-12186-9_16CrossRefGoogle Scholar
  6. 6.
    vanden Broucke, S.K., De Weerdt, J.: Decis. Support Syst. Fodina: a robust and flexible heuristic process discovery technique 100, 109–118 (2017)Google Scholar
  7. 7.
    Buijs, J.C., Van Dongen, B.F., Van Der Aalst, W.M.: A genetic algorithm for discovering process trees. In: CEC 2012, June, pp. 1–8. IEEE (2012)Google Scholar
  8. 8.
    Conforti, R., Dumas, M., García-Bañuelos, L., La Rosa, M.: BPMN miner: automated discovery of BPMN process models with hierarchical structure. Inf. Syst. 56, 284–303 (2016)CrossRefGoogle Scholar
  9. 9.
    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
  10. 10.
    Fournier-Viger, P., Chun, J., Lin, W., Kiran, R.U., Koh, Y.S., Thomas, R.: A survey of sequential pattern mining. Ubiquit. Int. 1(1), 54–77 (2017)Google Scholar
  11. 11.
    Greco, G., Guzzo, A., Pontieri, L., Saccà, D.: Discovering expressive process models by clustering log traces. IEEE TKDE 18, 1010–1027 (2006)Google Scholar
  12. 12.
    Günther, C.W., van der Aalst, W.M.P.: Fuzzy mining – adaptive process simplification based on multi-perspective metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 328–343. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-75183-0_24CrossRefGoogle Scholar
  13. 13.
    Leemans, M., van der Aalst, W.M.P.: Discovery of frequent episodes in event logs. In: Ceravolo, P., Russo, B., Accorsi, R. (eds.) SIMPDA 2014. LNBIP, vol. 237, pp. 1–31. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-27243-6_1CrossRefGoogle Scholar
  14. 14.
    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
  15. 15.
    Maggi, F.M., Mooij, A.J., Van Der Aalst, W.M.: User-guided discovery of declarative process models. In: CIDM 2011, April, pp. 192–199. IEEE (2011)Google Scholar
  16. 16.
    Pei, J., et al.: Mining sequential patterns by pattern-growth: the PrefixSpan approach. IEEE Trans. Knowl. Data Eng. 16, 1424–1440 (2004)CrossRefGoogle Scholar
  17. 17.
    Senderovich, A., Weidlich, M., Gal, A.: Temporal network representation of event logs for improved performance modelling in business processes. In: Carmona, J., Engels, G., Kumar, A. (eds.) BPM 2017. LNCS, vol. 10445, pp. 3–21. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-65000-5_1CrossRefGoogle Scholar
  18. 18.
    Solé, M., Carmona, J.: Process mining from a basis of state regions. In: Lilius, J., Penczek, W. (eds.) PETRI NETS 2010. LNCS, vol. 6128, pp. 226–245. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-13675-7_14CrossRefzbMATHGoogle Scholar
  19. 19.
    Song, M., Günther, C.W., van der Aalst, W.M.P.: Trace clustering in process mining. In: Ardagna, D., Mecella, M., Yang, J. (eds.) BPM 2008. LNBIP, vol. 17, pp. 109–120. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-00328-8_11CrossRefGoogle Scholar
  20. 20.
    Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: Apers, P., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 1–17. Springer, Heidelberg (1996).  https://doi.org/10.1007/BFb0014140CrossRefGoogle Scholar
  21. 21.
    Tax, N., Dalmas, B., Sidorova, N., van der Aalst, W.M., Norre, S.: Interest-driven discovery of local process models. Inf. Syst. 77, 105–117 (2018)CrossRefGoogle Scholar
  22. 22.
    Tax, N., Sidorova, N., Haakma, R., van der Aalst, W.M.: Mining local process models. J. Innov. Digit. Ecosyst. 3(2), 183–196 (2016)CrossRefGoogle Scholar
  23. 23.
    Weijters, A.J.M.M., Ribeiro, J.T.S.: Flexible heuristics miner (FHM). In: CIDM 2011, pp. 310–317 (2011)Google Scholar
  24. 24.
    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

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Univ. Paris-Dauphine, CNRS UMR[7243], LAMSADEParisFrance
  2. 2.Humboldt-Universität zu BerlinBerlinGermany

Personalised recommendations