Publicação
Transparent Business Process Outcome Prediction using a Graph Stochastic Attention Mechanism
| Resumo: | Predictive Process Monitoring (PPM) aims to predict the future states of ongoing process instances. A primary objective is to accurately predict process outcomes while ensuring decision transparency, which is critical for enhancing process efficiency and reducing operational risk. Existing interpretable approaches to process monitoring often struggle with balancing transparency and reliability. Specifically, approaches that prioritize transparency often fall short in predictive accuracy and generalization, while those that achieve higher prediction performance often provide less reliable explanations. To address these limitations, we propose a novel Transparent Process Outcome Prediction framework (TPOP) using a graph neural network with stochastic attention. We begin by applying a SHAP-based feature selection technique to identify and extract the most relevant attributes from the log, thereby improving the quality of graph-based process representations. Next, we introduce a graph stochastic attention mechanism, which helps the model in concentrate on key paths and activities during training, leading to transparent and trustworthy predictions. Experimental evaluations on ten real-life event logs demonstrate that our approach outperforms state-of-the-art approaches in both predictive performance and interpretability. Furthermore, by visualizing how specific activities influence process outcomes across various cases, we confirm the reliability of the explanations generated by our approach. |
|---|---|
| Autores principais: | Zhang, Xiwei |
| Outros Autores: | Fang, Xianwen; Gong, Jianhua; Mao, Gubao; Liu, Cong |
| Assunto: | Feature selection graph stochastic attention outcome prediction predictive process monitoring process mining transparent interpretability Hardware and Architecture Computer Science Applications Computer Networks and Communications Information Systems and Management |
| Ano: | 2026 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | Predictive Process Monitoring (PPM) aims to predict the future states of ongoing process instances. A primary objective is to accurately predict process outcomes while ensuring decision transparency, which is critical for enhancing process efficiency and reducing operational risk. Existing interpretable approaches to process monitoring often struggle with balancing transparency and reliability. Specifically, approaches that prioritize transparency often fall short in predictive accuracy and generalization, while those that achieve higher prediction performance often provide less reliable explanations. To address these limitations, we propose a novel Transparent Process Outcome Prediction framework (TPOP) using a graph neural network with stochastic attention. We begin by applying a SHAP-based feature selection technique to identify and extract the most relevant attributes from the log, thereby improving the quality of graph-based process representations. Next, we introduce a graph stochastic attention mechanism, which helps the model in concentrate on key paths and activities during training, leading to transparent and trustworthy predictions. Experimental evaluations on ten real-life event logs demonstrate that our approach outperforms state-of-the-art approaches in both predictive performance and interpretability. Furthermore, by visualizing how specific activities influence process outcomes across various cases, we confirm the reliability of the explanations generated by our approach. |
|---|