Remaining Time Prediction of Business Processes based on Multilayer Machine Learning

Remaining time predictive monitoring of business processes (BPs) is a key research issue in business process mining, which provides timely predictive information for stakeholders to take proactive corrective actions to reduce process execution risk such as exceeding time limit or to adjust the priority of activities. However, current researches on remaining time prediction only consider the impact of internal attributes of single process instance, but ignore the resource competition among multiple instances executed together. Therefore, this paper takes resource competition into consideration and characterizes several inter-instance attributes as the input of prediction. We also prioritize and select some key activities that strongly impact the execution time of BPs according to historical event logs and include them as input of the prediction. Meanwhile, in order to solve the instability of one single prediction model in complex scenarios, a multilayer hybrid model constructed from XGBoost and LightGBM models using stacking technique is proposed. Experiments on four real-life datasets show that our approach of considering attributes among instances and including key activities into a hybrid model outperforms other prediction methods.