In this paper, we address the "black-box" problem in predictive process analytics by building interpretable models that are capable to inform both what and why is a prediction. Predictive process analytics is a newly emerged discipline dedicated to providing business process intelligence in modern organisations. It uses event logs, which capture process execution traces in the form of multi-dimensional sequence data, as the key input to train predictive models. These predictive models, often built upon deep learning techniques, can be used to make predictions about the future states of business process execution. We apply attention mechanism to achieve model interpretability. We propose i) two types of attentions: event attention to capture the impact of specific process events on a prediction, and attribute attention to reveal which attribute(s) of an event influenced the prediction; and ii) two attention mechanisms: shared attention mechanism and specialised attention mechanism to reflect different design decisions in when to construct attribute attention on individual input features (specialised) or using the concatenated feature tensor of all input feature vectors (shared). These lead to two distinct attention-based models, and both are interpretable models that incorporate interpretability directly into the structure of a process predictive model. We conduct experimental evaluation of the proposed models using real-life dataset, and comparative analysis between the models for accuracy and interpretability, and draw insights from the evaluation and analysis results.
Time-Aware and Transition-Semantic Graph Neural Networks for Interpretable Predictive Business Process Monitoring
College of Computing and Mathematical Sciences, Khalifa University（卡利法大学计算与数学科学学院）；Department of Computer Science, Università degli Studi di Milano（米兰大学计算机科学系）
An Innovative Next Activity Prediction Using Process Entropy and Dynamic Attribute-Wise-Transformer in Predictive Business Process Monitoring
Department of Electrical and Computer Engineering, Faculty of Engineering and Computer Science, University of Victoria（维多利亚大学工程与计算机科学学院电气与计算机工程系）；Department of Mechanical Engineering, Faculty of Engineering and Computer Science, University of Victoria（维多利亚大学工程与计算机科学学院机械工程系）
CrunchLLM: Multitask LLMs for Structured Business Reasoning and Outcome Prediction
Department of Computer Science University of Kentucky（计算机科学系 俄亥俄大学）；Department of Computer Science, Institute for Biomedical Informatics University of Kentucky（计算机科学系 生物医学信息学研究所 俄亥俄大学）
Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning
Cooperative Innovation Center, Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所协同创新中心）；University of Chinese Academy of Sciences（中国科学院大学）；JD Technology Group（京东科技集团）；University of Technology Sydney（悉尼科技大学）
All Eyes on the Workflow: Automated and Efficient Event Discovery from Video Streams
Chair of Process and Data Science, RWTH Aachen University（过程与数据科学教授席位，亚琛工业大学）；Artificial Intelligence & Machine Learning Lab, Technical University of Darmstadt（人工智能与机器学习实验室，达姆施塔特技术大学）
Agentic Workflow Using RBA$_θ$ for Event Prediction
Department of Electrical and Computer Engineering, Aarhus University（电气与计算机工程系，阿arhus大学）；Department of Electrical, IT and Cybernetics, University of South-Eastern Norway（电气、信息技术与自动化系，南东挪威大学）
