Advancing Object-Centric Process Mining with Multi-Dimensional Data Operations

Abstract

Analyzing process data at varying levels of granularity is important to derive actionable insights and support informed decision-making. Object-Centric Event Data (OCED) enhances process mining by capturing interactions among events and multiple objects, leading to the discovery of more detailed and realistic yet complex process models. The lack of methods to adjust the granularity of the analysis prevents users from leveraging the full potential of Object-Centric Process Mining (OCPM). To address this gap, we propose four operations: drill-down, roll-up, unfold, and fold, which enable analysts to change the granularity of analysis when working with Object-Centric Event Logs (OCEL). These operations allow analysts to seamlessly transition between detailed and aggregated process models, facilitating the discovery of insights that require varying levels of abstraction. We formally define these operations and implement them in an open-source Python library. To validate their utility, we applied the approach to real-world OCEL data extracted from a learning management system, covering a four-year period and approximately 400 students, as a case of object-centric educational process mining. This case study shows significant improvements in the precision and fitness of the discovered models after applying the operations. In addition, we evaluate the scalability of the operators on large, publicly available OCELs derived from the Business Process Intelligence Challenge datasets, demonstrating that the operations remain computationally feasible on industrial-scale event logs. This approach can empower analysts to perform more flexible and comprehensive process exploration, unlocking actionable insights through flexible granularity adjustments.