Spenrath, Y., & Hassani, M. (2020). Predicting Business Process Bottlenecks In Online Events Streams Under Concept Drifts. In M. Steglich, C. Muller, G. Neumann, & M. Walther (Eds.), Proceedings of European Council for Modelling and Simulation (ECMS) 2020 (pp. 190-196). (Proceedings European Council for Modelling and Simulation; Vol. 34, No. 1). European Council for Modeling and Simulation. https://doi.org/10.7148/2020-0190
Process performance analysis is an important subtask of process mining that aims at optimizing the discovered process models. In this paper we focus on improving process throughput by predicting congestions in the process execution (bottlenecks). We discuss an ongoing work on incorporating gradual and seasonal concept drift in this bottleneck prediction. In the field of process mining, we develop a method of predicting whether and which bottleneck will likely appear based on data known before a case starts. We introduce GRAHOF, a Gradual and Recurrent Adaptive Hoeffding Option Forest approach, which adapts to gradual and seasonal concept drifts when predicting bottlenecks of business processes in an online setting. We evaluate the parameters involved in GRAHOF using a synthetic event stream and a real-world event log.