Transforming Event Knowledge Graph to Object-Centric Event Logs: A Comparative Study for Multi-dimensional Process Analysis.

Process mining has significantly transformed business process management by introducing innovative data-based analysis techniques and empowering organizations to unveil hidden insights previously buried within their recorded data. The analysis is conducted on event logs structured by conceptual models. Traditional models were defined based on only a single case notion, e.g., order or item in the purchase process. This limitation hinders the application of process mining in practice for which new data models are developed, a.k.a, multi-dimensional Event Knowledge Graph (EKG) and Object-Centric Event Log (OCEL). While several tools have been developed for OCEL, there is a lack of process mining tooling around the EKG. In addition, there is a lack of comparison about the practical implication of choosing one approach over the other. To fill this gap, the contribution of this paper is threefold. First, it defines and implements an algorithm to transform event logs represented as EKG to OCEL. The implementation is then used to transform five real event logs based on which the approach is evaluated. Second, it compares the performance of analyzing event logs represented in these two models. Third, it reveals similarities and differences in analyzing processes based on event logs represented in these two models. The results highlight ten important findings, including different approaches in calculating directly-follows relations when analyzing filtered event logs in these models and issues that need to be considered in analyzing event lifecycle and inter-log relations using OCEL.