用于推荐下一个最佳操作的规定性业务流程监控
Prescriptive Business Process Monitoring for Recommending Next Best Actions
摘要：
预测性业务流程监控（PBPM）技术基于历史事件日志数据预测未来的流程行为，以改进运营业务流程。关于下一个活动预测，最近的PBPM技术使用最先进的深度神经网络（DNN）来学习预测模型，以在运行的过程实例中产生更准确的预测。尽管组织通过关键绩效指标（KPI）来衡量流程绩效，但DNN的学习过程并不直接受其影响。因此，所得到的下一个最可能的活动预测在实践中可能不太有益。规范性业务流程监控（PrBPM）方法评估有关其对流程性能（通常由KPI测量）的影响的预测，以通过发出警报或建议操作来防止不希望的流程活动。然而，这些方法都没有推荐实际的过程活动作为根据给定KPI优化的动作。我们提出了一种PrBPM技术，它将下一个最有可能的活动转换为给定KPI的下一个最佳行动。因此，我们的技术使用业务流程模拟，以确保控制流一致性的建议行动。基于我们的评估与两个现实生活中的事件日志，我们表明，我们的技术的下一个最好的行动可以优于下一个活动预测的KPI的优化和距离的实际过程实例。
英文摘要：
Predictive business process monitoring (PBPM) techniques predict future process behaviour based on historical event log data to improve operational business processes. Concerning the next activity prediction, recent PBPM techniques use state-of-the-art deep neural networks (DNNs) to learn predictive models for producing more accurate predictions in running process instances. Even though organisations measure process performance by key performance indicators (KPIs), the DNN`s learning procedure is not directly affected by them. Therefore, the resulting next most likely activity predictions can be less beneficial in practice. Prescriptive business process monitoring (PrBPM) approaches assess predictions regarding their impact on the process performance (typically measured by KPIs) to prevent undesired process activities by raising alarms or recommending actions. However, none of these approaches recommends actual process activities as actions that are optimised according to a given KPI. We present a PrBPM technique that transforms the next most likely activities into the next best actions regarding a given KPI. Thereby, our technique uses business process simulation to ensure the control-flow conformance of the recommended actions. Based on our evaluation with two real-life event logs, we show that our technique`s next best actions can outperform next activity predictions regarding the optimisation of a KPI and the distance from the actual process instances.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
