用扩散模型从随机日志中恢复流程轨迹，提升噪声下的可靠性。
DDTR: Diffusion Denoising Trace Recovery
- 基于扩散去噪框架，融合过程知识进行轨迹重建
- 相比现有方法最高提升25%准确率，高噪声下更稳健
- 适合处理传感器或模型预测带来的不确定性日志
随着技术发展，传统确定性流程日志正从不确定源（如不精确传感器或基于摄像头的机器学习预测模型）中采集。面对具有概率信息的随机日志，需开展随机轨迹恢复以可靠理解系统运行机制。本文提出一种基于扩散去噪概率模型（DDPM）的新深度学习方法，通过隐式发现或显式注入过程知识，在训练阶段实现轨迹去噪恢复。实证评估显示，该方法在性能上达到当前最优，相较已有方法最高提升25%，且在高噪声环境下表现出更强鲁棒性。
原文摘要 · Abstract (English)
With recent technological advances, process logs, which were traditionally deterministic in nature, are being captured from non-deterministic sources, such as uncertain sensors or machine learning models (that predict activities using cameras). In the presence of stochastically-known logs, logs that contain probabilistic information, the need for stochastic trace recovery increases, to offer reliable means of understanding the processes that govern such systems. We design a novel deep learning approach for stochastic trace recovery, based on Diffusion Denoising Probabilistic Models (DDPM), which makes use of process knowledge (either implicitly by discovering a model or explicitly by injecting process knowledge in the training phase) to recover traces by denoising. We conduct an empirical evaluation demonstrating state-of-the-art performance with up to a 25% improvement over existing methods, along with increased robustness under high noise levels.
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