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Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning
Decision Support Systems ( IF 7.5 ) Pub Date : 2021-01-11 , DOI: 10.1016/j.dss.2021.113494
Kai Heinrich , Patrick Zschech , Christian Janiesch , Markus Bonin

Predicting next events in predictive process monitoring enables companies to manage and control processes at an early stage and reduce their action distance. In recent years, approaches have steadily moved from classical statistical methods towards the application of deep neural network architectures, which outperform the former and enable analysis without explicit knowledge of the underlying process model. While the focus of prior research was on the long short-term memory network architecture, more deep learning architectures offer promising extensions that have proven useful for other applications of sequential data. In our work, we introduce a gated convolutional neural network and a key-value-predict attention network to the task of next event prediction. In a comprehensive evaluation study on 11 real-life benchmark datasets, we show that these two novel architectures surpass prior work in 34 out of 44 metric-dataset combinations. For our evaluation, we consider the effects of process data properties, such as sparsity, variation, and repetitiveness, and discuss their impact on the prediction quality of the different deep learning architectures. Similarly, we evaluate their classification properties in terms of generalization and handling class imbalance. Our results provide guidance for researchers and practitioners alike on how to select, validate, and comprehensively benchmark (novel) predictive process monitoring models. In particular, we highlight the importance of sufficiently diverse process data properties in event logs and the comprehensive reporting of multiple performance indicators to achieve meaningful results.



中文翻译:

过程数据属性至关重要:引入门控卷积神经网络(GCNN)和键值预测注意力网络(KVP)以进行深度学习的下一事件预测

在预测性过程监控中预测下一个事件可使公司在早期阶段管理和控制过程并缩短其行动距离。近年来,方法已经从经典的统计方法稳步发展到了深度神经网络体系结构的应用,后者的性能优于前者,并且可以在不了解底层过程模型的情况下进行分析。尽管先前研究的重点是长期的短期内存网络体系结构,但更深入的学习体系结构提供了有希望的扩展,这些扩展已被证明对顺序数据的其他应用有用。在我们的工作中,我们将门控卷积神经网络和键值预测注意力网络引入到下一个事件预测的任务中。在对11个现实基准数据集的综合评估研究中,我们表明,这两种新颖的体系结构在44个度量数据集组合中的34个中超过了先前的工作。为了进行评估,我们考虑了过程数据属性(例如稀疏性,变异性和重复性)的影响,并讨论了它们对不同深度学习架构的预测质量的影响。同样,我们根据泛化和处理类不平衡来评估其分类属性。我们的结果为研究人员和从业人员提供有关如何选择,验证和全面基准化(新颖)预测性过程监控模型的指南。特别是,我们强调了事件日志中足够多样化的过程数据属性以及全面报告多个性能指标以实现有意义的结果的重要性。

更新日期:2021-02-21
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