Bridging the Gap Between Traditional Process Mining and Object-Centric Process Mining

Process mining has become an essential technique for analyzing and optimizing business processes by leveraging digital traces recorded by enterprise systems. However, traditional process mining methods rely heavily on the concept of case identifiers, assuming that each event is associated with only one process instance. This assumption often limits their applicability in complex, real-world environments where multiple objects interact concurrently. This study seeks to connect conventional process mining approaches with the growing domain of object-centric process mining, which provides a broader perspective by considering events linked to multiple business entities. We review the conceptual foundations of both approaches and identify the challenges in transitioning from a case-centric to an object-centric perspective. Our findings demonstrate that object-centric process mining provides richer insights into interconnected process behavior. We conclude that object-centric paradigms mark a significant advancement in process analytics, paving the way for more adaptive and intelligent process improvement frameworks. This study not only bridges conventional process mining approaches with the emerging field of object-centric process mining (OC-PM) but also explores how recent advancements, particularly in Generative AI, are being leveraged within OC-PM frameworks. Specifically, we highlight approaches that integrate Generative AI techniques, including Large Language Models (LLMs), to enhance process understanding and prediction. The integration of AI—especially Generative AI—enables researchers and practitioners to move beyond the limitations and challenges of classical, case-centric process mining, offering more flexible, intelligent, and context-aware process analysis capabilities.