基于行为轮廓矩阵增强的业务流程结果预测方法

预测性过程监控依赖于预测效果，针对如何增强预测性过程监控预测效果的问题，提出了一种基于行为轮廓矩阵增强的业务流程结果预测方法。首先，通过分析活动间的行为关系提取行为轮廓矩阵，并将其与事件序列一同输入到模型中。随后，结合卷积神经网络（CNN）和长短期记忆网络（LSTM）分别学习矩阵图像特征和序列特征。最后，引入注意力机制以整合图像特征和序列特征进行预测。通过真实事件日志进行验证，在预测事件日志结果方面，提出的增强方法对比基准的LSTM预测方法提高了预测效果，验证了方法的可行性。该方法结合行为轮廓矩阵增强了预测模型对事件日志中行为之间关系的理解，进而提升了预测效果。

Method for business process outcome prediction based on behavior profile matrix enhancement

Predictive process monitoring(PPM) relies on predictive effectiveness, and to address the challenge of improving predictive performance in PPM, this paper proposed a novel approach called behavior profile matrix enhanced business process outcome prediction. Initially, this approach extracted the behavior profile matrix by analyzing the interactions among activities and incorporated it into the model along with event sequences. Then, it used convolutional neural networks(CNN) and long short-term memory networks(LSTM) to independently capture image features from the matrix and sequence features. Finally, this approach integrated an attention mechanism to seamlessly combine both image and sequence features for predictive purposes. Validation using real event logs demonstrates that the proposed enhancement method significantly enhances predictive performance compared to the baseline LSTM prediction methods when forecasting event log outcomes, confirming the feasibility of this approach. This approach combines the behavior profile matrix to enhance the predictive model''s understanding of relationships between behaviors in event logs, consequently leading to an improvement in predictive performance.