Paper titled “Interactive Data-driven Process Model Construction” has been accepted for publication at ER 2018

@Conference{Dixit18a,
Title = {Interactive Data-driven Process Model Construction},
Author = {Dixit, P. M. and Verbeek, H. M. W and Buijs, J. C. A. M. and Aalst, W. M. P. v. d.},
Booktitle = {37th International Conference on Conceptual Modeling, ER 2018, Proceedings},
Year = {2018},
Editor = {Li, Z., and Trujillo, J. C., and Du, X. and Lee, M. L. and Davis, K. C. and Ling, T. W. and Li, G.},
Month = {October},
Pages = {251--265},
Publisher = {Springer},
Series = {LNCS},
Volume = {11157},
Abstract = {Process discovery algorithms address the problem of learning process models from event logs. Typically, in such settings a user's activity is limited to conguring 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.},
Comment = {Accepted for publication},
Doi = {10.1007/978-3-030-00847-5_19},
Owner = {hverbeek},
Timestamp = {2018.06.18}
}