Problem
Research questions and friction points this paper is trying to address.
Predictive Process Monitoring
Multi-Task Learning
Single-Task Learning
Task Synergy
Model Maintenance
On the Potential of Multi-Task Learning in Predictive Process Monitoring
🤖 AI Summary
研究探讨了多任务学习(MTL)在预测过程监控(PPM)中的潜力，通过对比单任务学习(STL)，发现MTL在下一活动预测和缓解类别不平衡方面有显著优势。
📝 Abstract
Predictive Process Monitoring (PPM) forecasts how ongoing organizational processes unfold, enabling information systems to move beyond execution support toward proactive analysis and monitoring. Although deep learning has improved prediction accuracy in PPM, most approaches follow a single-task learning (STL) setup, training a separate model per task. This increases maintenance effort and overlooks potential synergies. Multi-task learning (MTL), which jointly learns multiple prediction targets in one model, offers a promising alternative, yet its effectiveness in PPM remains underexplored. It remains unclear whether and under which settings MTL improves upon STL, which prediction tasks benefit most from joint learning, which task combinations are particularly synergistic, and if and how tasks should be balanced. To fill this gap, we present the first comprehensive empirical study of MTL for PPM, evaluating a variety of task combinations, neural architectures, and optimization methods. Overall, our results position MTL as a strong paradigm for PPM: we see substantial improvements in next-activity prediction and inherent mitigation of class imbalance using MTL, while task balancing is especially critical under low-capacity models.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-Task Learning
Predictive Process Monitoring
Next-Activity Prediction
Class Imbalance
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