基于概念漂移检测的数字孪生流程预测模型.

Predictive process monitoring can provide timely information during the operation of business processes, in order to take measures to address potential risks. How to improve the accuracy of process prediction has always been highly concerned. Most of the existing research methods focus on process prediction in static environments, with few combining digital twin technology for process prediction in dynamic environments. To this end, this paper proposed a method based on concept drift detection and constructed a digital twin process prediction model to predict the next activity. Firstly, this method used behavioral relationship between event streams and weight divergence to extract features from activities in the process and obtained the feature sets of data flows. Secondly, this method performed drift detection. It dynamically selected feature sets and input them into the artificial intelligence model for training and predicting the next activity. Then, it used advanced technologies such as the Internet of Things and cloud computing to create a digital twin virtual environment. Finally, this paper obtained a digital twin model based on concept drift. It carried out evaluation and analysis on publicly available datasets, and the experimental results show that the proposed method can improve the effectiveness of prediction. [ABSTRACT FROM AUTHOR]