DDTR: Diffusion Denoising Trace Recovery

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. The goal of process mining is to discover, analyze, and optimize real-world processes [1]. Servicing a patient in a hospital, filing a marriage certificate, or preparing a meal are all examples of real-world processes that can be analyzed and improved. Process mining relies on a process log, a recording of process execution, which contains a collection of traces where each trace is a sequence of activities. Traditionally, a process log is created by either manually logging real-world activities (e.g., a nurse keying in the timestamp of finishing an examination), or having activities automatically captured and logged by an information system. Process logs are assumed to record events in a deterministic fashion.