-Bridging the Gap: A Decision Framework for Traditional Process Mining vs. Object-Centric Process Mining

As business processes grow more complex and interconnected, organizations face increasing pressure to choose the right analytical tools to understand their opera- tions. Traditional Process Mining (TPM), which relies on case-centric event logs, has long been the standard approach due to its tool maturity and ease of use. However, it often struggles to capture the nuances of multi-entity systems. Object-Centric Pro- cess Mining (OCPM) offers an alternative by preserving relationships between mul- tiple object types, enabling more detailed insights into coordination and concurrency. This thesis explores the comparative strengths and limitations of TPM and OCPM through a mixed-methods approach. A literature-based framework was developed and applied to two contrasting datasets: a structured administrative workflow from the BPI Challenge 2017 and a complex, dynamic object-centric log derived from Age of Empires II game telemetry. The comparison focused on eight analytical di- mensions, including scalability, model complexity, and interpretability. Based on these findings, a decision framework is proposed to help practitioners identify which technique is more suitable for their context. While TPM remains a strong option for straightforward processes and fast implementation, OCPM proves advantageous in capturing inter-object interactions and revealing deeper insights in complex environments. The framework aims to support more informed, case-specific method selection in both academic and applied process mining work.