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.
翻译:预测性监测是流程采矿的一个子领域,其目的是预测一个运行中案例在未来如何展开。其主要挑战之一是预测从某个时间点 -- -- 后缀预测 -- -- 将发生的活动的顺序。后缀预测问题的大多数方法通过学习如何预测下一个活动来预测后缀,而不是在培训阶段从整个后缀中学习。本文件提出一个基于编码解码器模型的新结构,其关注机制是将前缀从推论阶段的表述学脱钩,仅预测后缀的活动。在推论阶段,这一结构扩大为超常搜索算法,改进了每个后缀索引活动的选择。我们的方法已经用12个公共活动日志对6个不同的状态提案进行了测试,表明它大大超过这些提案。