论文标题
使用上下文增强的过程日志对深神经网络架构进行下一个活动预测的经验比较
An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs
论文作者
论文摘要
研究人员提出了各种预测业务流程监控（PBPM）技术，旨在预测过程执行过程中的未来过程行为。尤其是，下一个活动预测的技术可以预测改善运营业务流程的巨大潜力。为了获得更准确的预测，这些技术有很多依赖深度神经网络（DNNS），并考虑有关该过程正在运行的上下文的信息。但是，PBPM文献中缺少对此类技术的深入比较，这使研究人员和从业人员无法为给定事件日志选择最佳解决方案。为了解决这个问题，我们从经验上评估了三个有希望的DNN体系结构的预测质量，结合了五种已验证的编码技术，并基于五个富含上下文的现实生活事件日志。我们提供了四个可以支持研究人员和从业人员设计新型PBPM技术来预测下一项活动的发现。
Researchers have proposed a variety of predictive business process monitoring (PBPM) techniques aiming to predict future process behaviour during the process execution. Especially, techniques for the next activity prediction anticipate great potential in improving operational business processes. To gain more accurate predictions, a plethora of these techniques rely on deep neural networks (DNNs) and consider information about the context, in which the process is running. However, an in-depth comparison of such techniques is missing in the PBPM literature, which prevents researchers and practitioners from selecting the best solution for a given event log. To remedy this problem, we empirically evaluate the predictive quality of three promising DNN architectures, combined with five proven encoding techniques and based on five context-enriched real-life event logs. We provide four findings that can support researchers and practitioners in designing novel PBPM techniques for predicting the next activities.
