Predicting the Next Process Event Using Convolutional Neural Networks

Abdulrhman H. Al-Jebrni · Hongming Cai · Lihong Jiang

20 citations0/0 files storedIEEE International Conference on Progress in Informatics and Computing

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Business Process Modeling and Analysis Forecasting Techniques and Applications Robotic Process Automation Applications

Abstract

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.

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References

2017Predictive Business Process Monitoring with LSTM Neural Networks 2010Time prediction based on process mining 2017Predicting process behaviour using deep learning 2017Deep learning in big data Analytics: A comparative study 2015Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes 2016Comprehensible Predictive Models for Business Processes1 2014Comparing and Combining Predictive Business Process Monitoring Techniques 2013A markov prediction model for data-driven semi-structured business processes

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