Process mining, a data-driven approach for analyzing, visualizing, and improving business processes using event logs, has emerged as a powerful technique in the field of business process management. Process forecasting is a sub-field of process mining that studies how to predict future processes and process models. In this paper, we introduce and motivate the problem of event log prediction and present our approach to solving the event log prediction problem, in particular, using the sequence-to-sequence deep learning approach. We evaluate and analyze the prediction outcomes on a variety of synthetic logs and seven real-life logs and show that our approach can generate perfect predictions on synthetic logs and that deep learning techniques have the potential to be applied in real-world event log prediction tasks. We further provide practical recommendations for event log predictions grounded in the outcomes of the conducted experiments.

翻译：过程挖掘是一种利用事件日志分析、可视化和改进业务流程的数据驱动方法，已成为业务流程管理领域的一项强大技术。过程预测是过程挖掘的一个子领域，研究如何预测未来过程及过程模型。本文提出并论证了事件日志预测问题，并展示了解决该问题的方法，特别是采用序列到序列深度学习方法。我们通过多种合成日志及七组真实日志对预测结果进行评估与分析，证明该方法能在合成日志上生成完美预测，且深度学习技术具备应用于真实事件日志预测任务的潜力。此外，我们基于实验结果，为事件日志预测提供了切实可行的实践建议。