Predictive monitoring is a subfield of process mining that aims to predict how a running case will unfold in the future. One of its main challenges is forecasting the sequence of activities that will occur from a given point in time -- suffix prediction -- . Most approaches to the suffix prediction problem learn to predict the suffix by learning how to predict the next activity only, not learning from the whole suffix during the training phase. This paper proposes a novel architecture based on an encoder-decoder model with an attention mechanism that decouples the representation learning of the prefixes from the inference phase, predicting only the activities of the suffix. During the inference phase, this architecture is extended with a heuristic search algorithm that improves the selection of the activity for each index of the suffix. Our approach has been tested using 12 public event logs against 6 different state-of-the-art proposals, showing that it significantly outperforms these proposals.

对业务流程的预测监控是流程挖掘的子领域，旨在预测下一个事件的特征或下一个事件的序列。虽然已经提出了基于深度学习的多种方法，主要是经常发生的神经网络和卷积神经网络，但它们都不是真正利用过程模型中可用的结构信息。本文提出了一种基于图形卷积网络和经常性神经网络的方法，所述内部网络从过程模型中使用信息。真实事件日志的实验评估表明，我们的方法更加一致，更优于当前的最先进的方法。