The Effects of Log Noise in Process Mining

Anandi Karunaratne, Artem Polyvyanyy, Alistair Moffat

Abstract: Process mining bridges the gap between theoretical models and real-world processes by extracting actionable insights from the event logs generated by business operations. The growing importance of process mining means that the presence of noise in event logs poses a significant obstacle, reducing the accuracy and dependability of analysis. Moreover, the concept of noise lacks a unified definition in process mining. To address that challenge, we present a comprehensive review of the literature to consolidate definitions of noise and establish a unified taxonomy, deriving a hierarchical decomposition framework of noise types, and discussing their origins. Building on this framework, we next introduce a noise injection tool, called SNIP, that enables controlled experimentation by simulating various noise scenarios in event logs, with our discussion here supported by a case study demonstrating the tool’s application. Lastly, we explore how established noise concepts from related disciplines can be applied to enrich process mining methodologies. Together, these contributions provide a structured foundation for understanding noise in event logs, promoting more robust and reliable process mining outcomes.

External IDs:doi:10.1109/access.2025.3635682