Despite the prevalence of complex loop structures in real-world processes, nested loops and their inter- action with multi-instance loops remain underexplored. Existing process mining approaches lack clear definitions and frameworks for representing hierarchical loops, often leading to their misinterpretation as flat repetitions or structures that may actually represent multi-instance behavior. This obscures process logic, limits root-cause analysis, and reduces analytical value. To address this gap, we propose a three-step methodology to construct enhanced event logs that explicitly capture hierarchical loop structures. This approach distinguishes nested loops from multi- instance behavior by leveraging recursive data relationships commonly found in enterprise systems. Grounded in industrial process mining experience, the framework improves transparency and supports a more accurate interpretation of complex process behavior. Through conceptual demonstration and experimental evaluation on engineered synthetic SAP data, we demonstrate how the approach reveals hidden loop structures and enhances interpretability. This work provides a research-in-progress foun- dation for structure-aware process mining and outlines directions for future automation, hybrid loop detection, and integration with object-centric process mining. *Status: This paper has been accepted for publication as a full paper in the peer-reviewed proceedings of the 2nd Asia-Pacific Symposium on Process and Artificial Intelligence (ASPAI 2026), Pohang, South Korea, and will be published in the CEUR Workshop Proceedings (CEUR-WS).
Huang et al. (Thu,) studied this question.