Business Process Remaining Time Prediction: An Approach Based on Bidirectional Quasi Recurrent Neural Network with Attention

Business process prediction can effectively facilitate enterprises to control processes and deliver high-quality services. As one of the core tasks of process prediction, remaining time prediction has been widely concerned by scholars. Currently, traditional long short-term memory(LSTM) neural networks have been used to predict the remaining time of business process instances. However, due to the lack of parallelism and limited modeling ability of LSTM in processing sequence data, the accuracy of prediction has further room to improve. In this paper, the remaining time prediction method based on bidirectional quasi-recurrent neural network with attention is proposed. Firstly, this method uses the bidirectional quasi-recurrent neural network to build the prediction model, and adds the attention mechanism to the model enhances the characteristic information of the bidirectional quasi-recurrent neural network output. Secondly, a training iteration strategy based on different length trace prefixes is designed, which solves the problem of the difference in the number of trace prefixes of different lengths. Finally, event representation learning method is proposed, to achieve vectors representation of similarity to the same traces and frequent events, improves the accuracy of the remaining time prediction. Experiments on five public event log datasets show this method has improved the accuracy of prediction by an average of nearly 15%, and the average training time is reduced by about 26%, compared with the existing methods. © 2022 Chinese Institute of Electronics. All rights reserved.