基于深度迁移学习的业务流程实例剩余执行时间预测方法

Predicting Remaining Business Time with Deep Transfer Learning

【目的】预测正在执行中的业务流程实例的剩余执行时间,为业务流程优化提供决策支持。【方法】提出一个业务流程实例剩余执行时间预测的深度迁移学习框架,该框架使用多层循环神经网络构建预测模型,并设计事件表示学习方法为神经网络提供预训练输入。【结果】在5个公开真实数据集上进行实验,结果表明本文方法与现有最优的基于流程模型和深度学习的方法相比,预测误差平均降低约11%。【局限】本文方法可解释性较差,这在一定程度上制约其现实应用场景。【结论】本文提出的深度迁移学习框架和事件表示学习方法能有效提升业务流程实例剩余执行时间预测的准确性。

[Objective]The paper tries to predict the remaining execution time of ongoing business process,aiming to provide better decision making support for process optimization.[Methods]We proposed a transfer learning framework for remaining time prediction,which constructed the prediction model with multi-layers recurrent neural networks.Then,we used representation learning method for events to pre-train the prediction model.[Results]We examined our model with five publicly available datasets and found the proposed approach outperforms the existing ones by 11%on average.[Limitations]The proposed model is of low interpretability,which limits its applications for real business management cases.[Conclusions]The proposed approach could help us predict remaining task processing time.