6/20/2023 0 Comments Deskovery iii![]() ![]() ![]() Springer, Heidelberg (2009)Įiben, A.E., Smith, J.E.: Introduction to evolutionary computing. Van Dongen, B.F., Alves de Medeiros, A.K., Wen, L.: Process Mining: Overview and Outlook of Petri Net Discovery Algorithms. Phd thesis, Eindhoven University of Technology (2007) Van Dongen, B.F.: Process Mining and Verification. In: Proceedings of the 2009 ACM Symposium on Applied Computing, SAC 2009, pp. IEEE (to appear, 2012)Ĭalders, T., Günther, C.W., Pechenizkiy, M., Rozinat, A.: Using minimum description length for process mining. In: Proceedings of the 2012 IEEE World Congress on Computational Intelligence. Springer, Heidelberg (2007)īuijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: A Genetic Algorithm for Discovering Process Trees. IEEE Computer Society (2011)īergenthum, R., Desel, J., Lorenz, R., Mauser, S.: Process Mining Based on Regions of Languages. In: IEEE International Enterprise Computing Conference (EDOC 2011), pp. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128–1142 (2004)Īdriansyah, A., van Dongen, B., van der Aalst, W.M.P.: Conformance Checking using Cost-Based Fitness Analysis. Van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: Workflow Mining: Discovering Process Models from Event Logs. Van der Aalst, W.M.P., de Medeiros, A.K.A., Weijters, A.J.M.M.: Genetic Process Mining. Van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2(2), 182–192 (2012) Van der Aalst, W., Adriansyah, A., van Dongen, B.: Replaying history on process models for conformance checking and performance analysis. However, it only makes sense to consider precision, generalization and simplicity if the replay fitness is acceptable.Ĭarmona, J., van Dongen, B.F., van der Aalst, W.M.P., Adriansyah, A., Munoz-Gama, J.: Alignment Based Precision Checking. We show that all dimensions are important for process discovery. This paper also presents the ETM algorithm which allows the user to seamlessly steer the discovery process based on preferences with respect to the four quality dimensions. Moreover, existing approaches can not steer the discovery process based on user-defined weights for the four quality dimensions. In this paper, we show that existing process discovery algorithms typically consider at most two out of the four main quality dimensions: replay fitness, precision, generalization and simplicity. ![]() Furthermore, several metrics exist to measure the complexity of a model irrespective of the log. At the same time, there are many other metrics that compare a model with recorded behavior in terms of the precision of the model and the extent to which the model generalizes the behavior in the log. Often, the quality of a process discovery algorithm is measured by quantifying to what extent the resulting model can reproduce the behavior in the log, i.e. Process discovery algorithms typically aim at discovering process models from event logs that best describe the recorded behavior. ![]()
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