论文标题
预测性监视中的后缀预测的编码器模型
Encoder-Decoder Model for Suffix Prediction in Predictive Monitoring
论文作者
论文摘要
预测监控是过程挖掘的子场，旨在预测未来运行案例的发展。它的主要挑战之一是预测将从给定时间点（后缀预测）出发的活动顺序。后缀预测问题的大多数方法都学会通过学习如何仅预测下一个活动，而不是在训练阶段从整个后缀学习来预测后缀。本文提出了一种基于编码器模型的新颖体系结构，其注意机制将前缀从推理阶段进行了表示，仅预测后缀的活动。在推论阶段，通过一种启发式搜索算法扩展了该体系结构，该算法改善了后缀每个索引的活动的选择。我们的方法已使用12个公共事件日志对6个不同的最先进的建议进行了测试，这表明它的表现明显胜过这些建议。
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.
