Yongwang Yuan1, Xiangwei Liu2,3,*, Ke Lu1,3
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1227-1252, 2024, DOI:10.32604/cmc.2023.046937
- 30 January 2024
Abstract Predictive Business Process Monitoring (PBPM) is a significant research area in Business Process Management (BPM) aimed at accurately forecasting future behavioral events. At present, deep learning methods are widely cited in PBPM research, but no method has been effective in fusing data information into the control flow for multi-perspective process prediction. Therefore, this paper proposes a process prediction method based on the hierarchical BERT and multi-perspective data fusion. Firstly, the first layer BERT network learns the correlations between different category attribute data. Then, the attribute data is integrated into a weighted event-level feature vector and More >
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