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Ensemble-Based Prediction of Business Processes Bottlenecks With Recurrent Concept Drifts
2019
International Conference on Extending Database Technology
Bottleneck prediction is an important sub-task of process mining that aims at optimizing the discovered process models by avoiding such congestions. This paper discusses an ongoing work on incorporating recurrent concept drift in bottleneck prediction when applied to a real-world scenario. In the field of process mining, we develop a method of predicting whether and which bottlenecks will likely appear based on data known before a case starts. We next introduce GRAEC, a carefully-designed
dblp:conf/edbt/SpenrathH19
fatcat:urwhnxhmlvgohmjjp5wo2bn4vu