Self-Att-BiLSTM:一种面向业务流程活动与时间的多任务预测方法

Self-Att-BiLSTM: A Multitask Prediction Method for Business Process Activities and Time

业务流程中事件日志的分析与预测可以为流程监控和管理提供决策信息,现有研究方法多针对特定单个任务预测,不同任务间预测方法的可迁移性不高。多任务预测可以共享多个任务间的信息,提升单个任务预测的精度,但现有研究对重复活动的多任务预测效果有待提高。针对以上问题,提出一种注意力机制与双向长短时记忆结合的深度神经网络模型,实现对业务流程中重复活动和时间的多任务预测。预测模型可以共享不同任务已经学到的特征表示,实现多任务并行训练。在多个数据集中对不同方法进行对比,结果表明,所提方法提高了预测效率和预测精度,尤其对重复活动的预测精度有较好提升。

Decision information for process monitoring and management can be obtained by analyzing and predicting the event log of the business process.The existing research methods are mostly targeted at specific single-task prediction,and the portability between different task prediction methods is not high.Through multitask prediction,information can be shared among multiple tasks,improving the single-task prediction accuracy.However,the multitask prediction effect of existing research on repetitive activities must be improved.Based on the aforementioned problems,we propose a depth neural network model combining the attention mechanism and bidirectional long-short term memory,achieving multitask prediction for repetitive activities and time associated with the business process.The proposed prediction model can share the learned feature representation of different tasks and achieve multitask parallel training.Comparison is performed by applying different methods on datasets.The obtained results demonstrate that the proposed method improves the prediction efficiency and accuracy,especially in case of repetitive activities.