Steady-state detection (SSD) is a crucial task in the analysis of complex and dynamic systems, as it enables the reliable assessment of system behavior by distinguishing between stable and unstable states. SSD techniques have been extensively studied and applied in various domains, including signal processing and industrial systems. However, their application within the information systems domain, particularly in process mining, has received little attention, even though business processes themselves can be regarded as complex socio-technical systems. In particular, event logs that capture the execution of business processes often contain data from both steady and nonsteady states. Mixing up these states can significantly affect the accuracy and reliability of insights from common process mining tasks, such as process performance analysis and process discovery. To address this problem, we propose a dedicated SSD framework for process mining and demonstrate how differentiating between distinct process states can enhance the accuracy and reliability of process mining insights. The SSD framework takes an event log as input and identifies the existing steady and non-steady states along with their corresponding time periods. We evaluate the framework through two experiments: one assessing accuracy using simulated event logs and another demonstrating its impact on three key process mining tasks: process performance analysis, process discovery, and remaining time prediction.

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