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arXiv · 2510.22553

DDTR: Diffusion Denoising Trace Recovery

Maximilian Matyash·Avigdor Gal·Arik Senderovich
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Abstract

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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BibTeXRIS

Maximilian Matyash, Avigdor Gal, Arik Senderovich. 2025-10-26. DDTR: Diffusion Denoising Trace Recovery. https://arxiv.org/abs/2510.22553

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