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Predicting the Next Process Event Using Convolutional Neural Networks

机译:使用卷积神经网络预测下一个过程事件

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Adding the feature of business process event prediction to information systems increases its productivity in the long run, enhances the quality of the taken decisions, and eliminates inconsistencies. Inspired by the previous works of employing deep learning approaches to predicting the next process event based on files of logged events, we propose the use of one-dimensional convolutional neural networks (1D CNN) to address the same problem. In the proposed approach we used a five-layer 1D CNN method to predict the next process event based on the previous instances. This paper compared the proposed approach with other approaches that used recurrent neural networks (RNN) with Long Short-Term Memory (LSTM) neural networks, and others on eight datasets. The proposed approach outperformed all the previous studies of the state-of-the-art in this domain on all the provided datasets.
机译:从长远来看,将业务流程事件预测的功能添加到信息系统可以提高其生产率,提高决策的质量,并消除不一致之处。受到以前采用深度学习方法基于已记录事件的文件来预测下一个过程事件的工作的启发,我们建议使用一维卷积神经网络(1D CNN)来解决相同的问题。在提出的方法中,我们使用了一个五层的一维CNN方法,根据先前的实例来预测下一个过程事件。本文将提出的方法与使用递归神经网络(RNN)和长短期记忆(LSTM)神经网络的其他方法进行了比较,并对其他八个数据集进行了比较。所提出的方法在所有提供的数据集上均胜过了该领域最新的研究。

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