Instance-Based Process Matching Using Event-Log Information

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10253)


Process model matching provides the basis for many process analysis techniques such as inconsistency detection and process querying. The matching task refers to the automatic identification of correspondences between activities in two process models. Numerous techniques have been developed for this purpose, all share a focus on process-level information. In this paper we introduce instance-based process matching, which specifically focuses on information related to instances of a process. In particular, we introduce six similarity metrics that each use a different type of instance information stored in the event logs associated with processes. The proposed metrics can be used as standalone matching techniques or to complement existing process model matching techniques. A quantitative evaluation on real-world data demonstrates that the use of information from event logs is essential in identifying a considerable amount of correspondences.


Process model matching Event logs Process similarity 


  1. 1.
    Antunes, G., Bakhshandeh, M., Borbinha, J., Cardoso, J., Dadashnia, S., Francescomarino, C., Dragoni, M., Fettke, P., Gal, A., Ghidini, C., et al.: The process model matching contest 2015. In: 6th EMISA Workshop, pp. 127–155 (2015)Google Scholar
  2. 2.
    Budanitsky, A., Hirst, G.: Evaluating wordnet-based measures of lexical semantic relatedness. Comput. Linguist. 32(1), 13–47 (2006)CrossRefzbMATHGoogle Scholar
  3. 3.
    Cayoglu, U., Dijkman, R.M., Dumas, M., Fettke, P., Garcıa-Banuelos, L., Hake, P., Klinkmüller, C., Leopold, H., Ludwig, A., Loos, P., et al.: The process model matching contest 2013. In: 4th International Workshop on Process Model Collections: Management and Reuse (PMC-MR 2013) (2013)Google Scholar
  4. 4.
    Dijkman, R.M., Dumas, M., Van Dongen, B., Käärik, R., Mendling, J.: Similarity of business process models: metrics and evaluation. Inf. Syst. 36(2), 498–516 (2011)CrossRefGoogle Scholar
  5. 5.
    Dongen, B.F.V.: BPI challenge 2015 (2015).
  6. 6.
    Duan, S., Fokoue, A., Hassanzadeh, O., Kementsietsidis, A., Srinivas, K., Ward, M.J.: Instance-based matching of large ontologies using locality-sensitive hashing. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012. LNCS, vol. 7649, pp. 49–64. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-35176-1_4 CrossRefGoogle Scholar
  7. 7.
    Dumas, M., La Rosa, M., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management. Springer, Heidelberg (2012)Google Scholar
  8. 8.
    Engmann, D., Maßmann, S.: Instance matching with COMA++. In: BTW Workshops, pp. 28–37 (2007)Google Scholar
  9. 9.
    Gal, A., Anaby-Tavor, A., Trombetta, A., Montesi, D.: A framework for modeling and evaluating automatic semantic reconciliation. VLDB J. Int. J. Very Large Data Bases 14(1), 50–67 (2005)CrossRefGoogle Scholar
  10. 10.
    Gal, A., Weidlich, M.: Model matching - processes and beyond. In: Zdravkovic, J., Kirikova, M., Johannesson, P. (eds.) CAiSE 2015. LNCS, vol. 9097, pp. 525–526. Springer, Cham (2015). Google Scholar
  11. 11.
    Gal, A.: Uncertain schema matching. Synth. Lect. Data Manag. 3(1), 1–97 (2011)CrossRefzbMATHGoogle Scholar
  12. 12.
    Galil, Z., Micali, S., Gabow, H.: An o(EV logV) algorithm for finding a maximal weighted matching in general graphs. SIAM J. Comput. 15(1), 120–130 (1986)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Klinkmüller, C., Leopold, H., Weber, I., Mendling, J., Ludwig, A.: Listen to me: improving process model matching through user feedback. In: Sadiq, S., Soffer, P., Völzer, H. (eds.) BPM 2014. LNCS, vol. 8659, pp. 84–100. Springer, Cham (2014). doi: 10.1007/978-3-319-10172-9_6 Google Scholar
  14. 14.
    Leopold, H., Niepert, M., Weidlich, M., Mendling, J., Dijkman, R., Stuckenschmidt, H.: Probabilistic optimization of semantic process model matching. In: Barros, A., Gal, A., Kindler, E. (eds.) BPM 2012. LNCS, vol. 7481, pp. 319–334. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-32885-5_25 CrossRefGoogle Scholar
  15. 15.
    Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. VLDB J. 10(4), 334–350 (2001)CrossRefzbMATHGoogle Scholar
  16. 16.
    Razali, N.M., Wah, Y.B., et al.: Power comparisons of shapiro-wilk, kolmogorov-smirnov, lilliefors and anderson-darling tests. J. Stat. Model. Anal. 2(1), 21–33 (2011)Google Scholar
  17. 17.
    Sagi, T., Gal, A., Weidlich, M.: Measuring expected integration effort in service composition. In: 2014 IEEE International Conference on Services Computing (SCC), pp. 645–652. IEEE (2014)Google Scholar
  18. 18.
    Salton, G., McGill, M.J.: Introduction to modern information retrieval (1986)Google Scholar
  19. 19.
    Wang, J., Wen, J., Lochovsky, F., Ma, W.: Instance-based schema matching for web databases by domain-specific query probing. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases, vol. 30, pp. 408–419. VLDB Endowment (2004)Google Scholar
  20. 20.
    Weidlich, M., Dijkman, R., Mendling, J.: The ICoP framework: identification of correspondences between process models. In: Pernici, B. (ed.) CAiSE 2010. LNCS, vol. 6051, pp. 483–498. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-13094-6_37 CrossRefGoogle Scholar
  21. 21.
    Weidlich, M., Sagi, T., Leopold, H., Gal, A., Mendling, J.: Predicting the quality of process model matching. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 203–210. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40176-3_16 CrossRefGoogle Scholar
  22. 22.
    Yujian, L., Bo, L.: A normalized levenshtein distance metric. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1091–1095 (2007)CrossRefGoogle Scholar
  23. 23.
    Zaiß, K., Schlüter, T., Conrad, S.: Instance-based ontology matching using regular expressions. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM 2008. LNCS, vol. 5333, pp. 40–41. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-88875-8_19 CrossRefGoogle Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer SciencesVU University AmsterdamAmsterdamThe Netherlands
  2. 2.Faculty of Industrial Engineering and Management, Technion – Israel Institute of TechnologyHaifaIsrael
  3. 3.Hewlett Packard LabsHaifaIsrael

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