Abstract
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.
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Notes
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Currently available in ProM Nightly Build at http://www.promtools.org/doku.php?id=nightly.
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See the collection of real-world event logs at 4TU Center for Research Data http://data.4tu.nl/repository/collection:event_logs_real.
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References
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)
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). https://doi.org/10.1007/978-3-319-42887-1_20
Buijs, J.C.A.M.: Flexible evolutionary algorithms for mining structured process models (2014)
Burattin, A.: Process Mining Techniques in Business Environments. LNBIP, vol. 207. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-17482-2
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 2011
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). https://doi.org/10.1007/978-3-642-03848-8_11
Leemans, S.J.J.: Robust process mining with guarantees. SIKS Dissertation Series No. 2017-12 (2017)
Polato, M., Sperduti, A., Burattin, A., et al.: Time and activity sequence prediction of business process instances. Computing, 1–27 (2018). https://doi.org/10.1007/s00607-018-0593-x
Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)
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). https://doi.org/10.1007/978-3-642-28108-2_19
van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-19345-3
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)
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)
Walicki, M., Ferreira, D.R.: Sequence partitioning for process mining with unlabeled event logs. Data Knowl. Eng. 70(10), 821–841 (2011)
Weidlich, M., Polyvyanyy, A., Mendling, J., Weske, M.: Causal behavioural profiles - efficient computation, applications, and evaluation. Fundam. Inform. 113(3–4), 399–435 (2011)
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Abbad Andaloussi, A., Burattin, A., Weber, B. (2018). Toward an Automated Labeling of Event Log Attributes. In: Gulden, J., Reinhartz-Berger, I., Schmidt, R., Guerreiro, S., Guédria, W., Bera, P. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2018 2018. Lecture Notes in Business Information Processing, vol 318. Springer, Cham. https://doi.org/10.1007/978-3-319-91704-7_6
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