论文标题
预测监控的事件日志抽样
Event Log Sampling for Predictive Monitoring
论文作者
论文摘要
预测过程监视是过程挖掘的子场，旨在估算运行过程实例的情况或事件功能。此类预测引起了流程利益相关者的重大兴趣。但是，用于预测性监测的最新方法需要对复杂的机器学习模型进行培训，这通常效率低下。本文提出了一个实例选择过程，该过程允许对预测模型进行采样培训过程实例。我们表明，我们的抽样方法可以显着提高下一个活动预测方法的训练速度，同时保持可靠的预测准确性水平。
Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. This paper proposes an instance selection procedure that allows sampling training process instances for prediction models. We show that our sampling method allows for a significant increase of training speed for next activity prediction methods while maintaining reliable levels of prediction accuracy.
