Interactive data-driven process model construction

Dixit, P. M., Verbeek, H.M.W., Buijs, J. C.A.M. & van der Aalst, W. M.P. (2018). Interactive data-driven process model construction. In Xiaoyong Du, Guoliang Li, Zhanhuai Li, Juan C. Trujillo, Tok Wang Ling, Karen C. Davis & Mong Li Lee (Eds.), Conceptual Modeling – 37th International Conference, ER 2018, Proceedings (pp. 251-265). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), No. 11157 LNCS). Springer.


Process discovery algorithms address the problem of learning process models from event logs. Typically, in such settings a user’s activity is limited to configuring the parameters of the discovery algorithm, and hence the user expertise/domain knowledge can not be incorporated during traditional process discovery. In a setting where the event logs are noisy, incomplete and/or contain uninteresting activities, the process models discovered by discovery algorithms are often inaccurate and/or incomprehensible. Furthermore, many of these automated techniques can produce unsound models and/or cannot discover duplicate activities, silent activities etc. To overcome such shortcomings, we introduce a new concept to interactively discover a process model, by combining a user’s domain knowledge with the information from the event log. The discovered models are always sound and can have duplicate activities, silent activities etc. An objective evaluation and a case study shows that the proposed approach can outperform traditional discovery techniques.

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