Transformer-based business process remaining time prediction and encoding approaches evaluation
The prediction of business process remaining time is of great significance to the prevention of process timeout risk.The application of the deep learning model improves the accuracy of the remaining time prediction.However,the advantages of the model are not fully utilized due to the single input information.In addition,an effective activity encoding approach can contain rich context to improve the prediction accuracy.In view of the above problems,this paper proposed a business process remaining time prediction approach based on Transformer.The Transformer network was first used to construct the remaining time prediction model,and the activity and time attributes are selected as the input features.Then,in order to capture the temporal relationship between business process activities,four mainstream natural language processing fields were selected to train the representation vectors of activities.The four approaches were evaluated from the perspectives of encoding adaptability and encoding dimension.Finally,six real event logs were used for experiments.The results show that the proposed approach,compared with the existing approaches,can significantly improve the prediction accuracy,that the activity encoding of continuous bag-of-word model(CBOW)achieves better prediction results,and that the encoding dimensions are recommended based on event logs of different scales.
Transformerbusiness processremaining time predictiondeep learningencoding method
NETL
NSTL
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