发表机构
Eindhoven University of Technology; University of Wuppertal（埃因霍温理工大学; 伍珀塔尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出D-TAIA框架，通过参数高效微调FM骨干网，结合DATL预训练与FAISS检索等技术，在四个真实事件日志上实现多任务PPM的SOTA或竞争力性能。
AI 中文摘要
预测过程监控（PPM）使组织能够预测未来的流程行为，例如正在进行的案例的下一个活动和剩余时间。在实践中，三种情况会导致现有方法性能下降，即数据稀缺、高流程熵和分布偏移。尽管基础模型（FMs），尤其是大语言模型（LLMs），通过广泛的序列推理提供了新的范式，但在这些条件下将其适配到多任务PPPM仍然是一个未解决的挑战。现有的基于FM的方法要么缺乏处理分布偏移的机制，要么依赖于直接回归头，这可能与连续时间预测任务在结构上不匹配。本文介绍了D-TAIA（领域感知训练与基于注意力的推理架构），这是一种通过对FM骨干网进行参数高效微调来完成下一个活动和剩余时间联合预测任务的框架。我们的方法结合了领域感知三元组损失（DATL）预训练和基于FAISS的最近邻检索用于剩余时间预测，并采用TAIA推理策略在微调期间保留预训练的序列推理能力。在四个真实事件日志上进行评估，与微调后的LLM和循环神经网络基线相比，D-TAIA始终表现出SOTA或具有竞争力的性能。消融研究证实，来自自然语言处理和计算机视觉的技术可以通过仅1000万参数的骨干网有效地迁移到PPM，尽管组件的贡献因数据集熵而异。
英文摘要
Predictive Process Monitoring (PPM) enables organizations to forecast future process behavior, such as the next activity and remaining time of ongoing cases. In practice, three conditions cause existing methods to degrade, namely data scarcity, high process entropy and distributional shift. While Foundation Models (FMs), especially Large Language Models (LLMs), offer a new paradigm through broad sequential reasoning, adapting them to multi-task PPM under these conditions remains an open challenge. Existing FM-based approaches either lack mechanisms for handling distributional shift or rely on direct regression heads that can be structurally misaligned with continuous time prediction tasks. This paper introduces D-TAIA (Domain-aware Training and Attention-based Inference Architecture), a framework for a joint next activity and remaining time prediction task via parameter-efficient fine-tuning of an FM backbone. Our approach combines domain-aware triplet loss (DATL) pre-training with FAISS-based nearest neighbor retrieval for remaining time prediction, and adopts the TAIA inference strategy to preserve pre-trained sequential reasoning during fine-tuning. Evaluated across four real-world event logs, D-TAIA consistently shows SOTA or competitive performance compared to a fine-tuned LLM and a recurrent neural network baseline. Ablation studies confirm that techniques from NLP and computer vision can be transferred effectively to PPM with only a 10M-parameter backbone, though component contributions vary by dataset entropy.
CommentsThis paper has been accepted for the AI4PM Workshop at ECML PKDD 2026 in Naples, Italy
