Process mining enables the reconstruction and evaluation of business processes based on digital traces in IT systems. An increasingly important technique in this context is process prediction. Given a sequence of events of an ongoing trace, process prediction allows forecasting upcoming events or performance measurements. In recent years, multiple process prediction approaches have been proposed, applying different data processing schemes and prediction algorithms. This study focuses on deep learning algorithms since they seem to outperform their machine learning alternatives consistently. Whilst having a common learning algorithm, they use different data preprocessing techniques, implement a variety of network topologies and focus on various goals such as outcome prediction, time prediction or control-flow prediction. Additionally, the set of log-data, evaluation metrics and baselines used by the authors diverge, making the results hard to compare. This paper attempts to synthesise the advantages and disadvantages of the procedural decisions in these approaches by conducting a systematic literature review.

流程挖掘使您能够基于IT系统中的数字跟踪来重建和评估业务流程。在这种情况下，一种越来越重要的技术是过程预测。给定正在进行的跟踪事件序列，过程预测允许预测即将发生的事件或性能测量。近年来，已经提出了使用不同的数据处理方案和预测算法的多种过程预测方法。这项研究专注于深度学习算法，因为它们似乎在性能上始终胜过其机器学习替代方法。尽管它们具有通用的学习算法，但它们使用不同的数据预处理技术，实现各种网络拓扑，并专注于各种目标，例如结果预测，时间预测或控制流预测。此外，作者使用的一组日志数据，评估指标和基准存在差异，因此难以比较结果。本文试图通过进行系统的文献综述来综合这些方法中程序决策的优缺点。