Selecting Publishing Points for the Optimal Sharing of Predictive Monitoring Information of a Service Process

In order to facilitate collaboration and improve service quality, progress information together with predicted information (Predictive Monitoring Information, PMI) should be published to partners or customers in a timely manner. Currently, the publishing points of PMI are assigned to certain positions empirically before the execution of a service process. However, because of the dynamic nature of service processes, a decision to publish PMI should be made based on the actual progress and predictions of the future. Therefore, an adaptive approach to select the publishing points of PMI for service processes is proposed. We design a method to quantify the benefits of notifying the delay risk at a point in the service process using long short-term memory (LSTM) neural networks. Furthermore, the deep reinforcement learning model is applied to select the publishing points for PMI dynamically. Experiments on a real-world dataset prove that our approach can select the optimal publishing points.