Toward an Automated Labeling of Event Log Attributes

  • Amine Abbad AndaloussiEmail author
  • Andrea BurattinEmail author
  • Barbara WeberEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 318)


Process mining aims at exploring the data produced by executable business processes to mine the underlying control-flow and data-flow. Most of the process mining algorithms assume the existence of an event log with a certain maturity level. Unfortunately, the logs provided by process unaware information systems often do not comply with the required maturity level, since they lack the notion of process instance, also referred in process mining as “case id”. Without a proper identification of the case id attribute in log files, the outcome of process mining algorithms is unpredictable. This paper proposes a new approach that aims to overcome this challenge by automatically inferring the case id attribute from log files. The approach has been implemented as a ProM plugin and evaluated with several real-world event logs. The results demonstrate a high accuracy in inferring the case id attribute.


Real-world Event Logs Process Instance Ratio Group Process Discovery Algorithms Causal Behavioural Profiles 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Adriansyah, A., Munoz-Gama, J., Carmona, J., van Dongen, B.F., van der Aalst, W.M.P.: Measuring precision of modeled behavior. IseB 13(1), 37–67 (2015)CrossRefGoogle Scholar
  2. 2.
    Bayomie, D., Helal, I.M.A., Awad, A., Ezat, E., ElBastawissi, A.: Deducing case IDs for unlabeled event logs. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 242–254. Springer, Cham (2016). Scholar
  3. 3.
    Buijs, J.C.A.M.: Flexible evolutionary algorithms for mining structured process models (2014)Google Scholar
  4. 4.
    Burattin, A.: Process Mining Techniques in Business Environments. LNBIP, vol. 207. Springer, Cham (2015). Scholar
  5. 5.
    Burattin, A., Vigo, R.: A framework for semi-automated process instance discovery from decorative attributes. In: 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 176–183. IEEE, April 2011Google Scholar
  6. 6.
    Ferreira, D.R., Gillblad, D.: Discovering process models from unlabelled event logs. In: Dayal, U., Eder, J., Koehler, J., Reijers, H.A. (eds.) BPM 2009. LNCS, vol. 5701, pp. 143–158. Springer, Heidelberg (2009). Scholar
  7. 7.
    Leemans, S.J.J.: Robust process mining with guarantees. SIKS Dissertation Series No. 2017-12 (2017)Google Scholar
  8. 8.
    Polato, M., Sperduti, A., Burattin, A., et al.: Time and activity sequence prediction of business process instances. Computing, 1–27 (2018).
  9. 9.
    Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)CrossRefGoogle Scholar
  10. 10.
    van der Aalst, W.M.P.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011, Part I. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012). Scholar
  11. 11.
    van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2010). Scholar
  12. 12.
    van der Aalst, W.M.: Mediating between modeled and observed behavior: the quest for the ‘right’ process: keynote. In: Proceedings - International Conference on Research Challenges in Information Science (2013)Google Scholar
  13. 13.
    van der Aalst, W.M.P., Adriansyah, A., van Dongen, B.: Replaying history on process models for conformance checking and performance analysis. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2(2), 182–192 (2015)CrossRefGoogle Scholar
  14. 14.
    Walicki, M., Ferreira, D.R.: Sequence partitioning for process mining with unlabeled event logs. Data Knowl. Eng. 70(10), 821–841 (2011)CrossRefGoogle Scholar
  15. 15.
    Weidlich, M., Polyvyanyy, A., Mendling, J., Weske, M.: Causal behavioural profiles - efficient computation, applications, and evaluation. Fundam. Inform. 113(3–4), 399–435 (2011)Google Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.DTU Compute, Software EngineeringTechnical University of DenmarkKongens LyngbyDenmark

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