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Remaining Time Prediction of Business Processes based on Multilayer Machine Learning

机译：基于多层机器学习的业务流程剩余时间预测

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摘要

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.
机译：剩余时间预测业务流程（BPS）是业务流程挖掘的关键研究问题，该问题为利益攸关方提供了适当的预测信息，以便采取主动纠正措施，以减少超出时间限制或调整活动的优先级。然而，目前对剩余时间预测的研究只考虑单个流程实例的内部属性的影响，而是忽略在一起执行的多个实例之间的资源竞争。因此，本文考虑资源竞争，并将若干实例属性表征为预测的输入。我们还优先考虑并选择根据历史事件日志的强烈影响BPS执行时间的一些关键活动，并将其作为预测的输入。同时，为了解决复杂场景中一个预测模型的不稳定性，提出了一种由XGBoost和使用堆叠技术的灯泡模型构成的多层混合模型。四个真实数据集的实验表明，我们在实例中考虑属性的方法，包括密钥活动到混合模型中优于其他预测方法。

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