Predictive Process Monitoring (PPM) aims at leveraging historic process execution data to predict how ongoing executions will continue up to their completion. In recent years, PPM techniques for the prediction of the next activities have matured significantly, mainly thanks to the use of Neural Networks (NNs) as a predictor. While their performance is difficult to beat in the general case, there are specific situations where background process knowledge can be helpful. Such knowledge can be leveraged for improving the quality of predictions for exceptional process executions or when the process changes due to a concept drift. In this paper, we present a Symbolic[Neuro] system that leverages background knowledge expressed in terms of a procedural process model to offset the under-sampling in the training data. More specifically, we make predictions using NNs with attention mechanism, an emerging technology in the NN field. The system has been tested on several real-life logs showing an improvement in the performance of the prediction task.

翻译：预测性过程监控（PPM）旨在利用历史过程执行数据，预测正在进行的执行将如何持续直至完成。近年来，用于预测下一步活动的PPM技术已显著成熟，这主要归功于使用神经网络（NN）作为预测器。虽然在一般情况下其性能难以被超越，但在特定情境下，背景过程知识能发挥重要作用。此类知识可用于改进异常过程执行或过程因概念漂移而变化时的预测质量。本文提出了一种符号[神经]系统，该系统利用以程序化过程模型形式表达的背景知识，来补偿训练数据中的欠采样问题。具体而言，我们使用带有注意力机制的神经网络（NN领域的新兴技术）进行预测。该系统已在多个真实日志上进行了测试，结果表明其预测任务的性能得到了提升。