Graph-based feature extraction on object-centric event logs

Process mining techniques have proven crucial in identifying performance and compliance issues. Traditional process mining, however, is primarily case-centric and does not fully capture the complexity of real-life information systems, leading to a growing interest in object-centric process mining. This paper presents a novel graph-based approach for feature extraction from object-centric event logs. In contrast to established methods for feature extraction from traditional event logs, object-centric logs present a greater challenge due to the interconnected nature of events related to multiple objects. This paper addresses this gap by proposing techniques and tools for feature extraction specifically designed for object-centric event logs. In this work, we focus on features pertaining to the lifecycle of the objects and their interaction. These features enable a more comprehensive understanding of the process and its inherent complexities. We demonstrate the applicability of our approach through its implementation in two significant areas: anomaly detection and throughput time prediction for objects in the process. Our results, based on four problems in a Procure-to-Pay process, affirm the potential of our proposed features in enhancing the scope of process mining. By effectively transforming object-centric event logs into numeric vectors, we pave the way for the application of a broader range of machine learning techniques, such as classification, prediction, clustering, and anomaly detection, thereby extending the capabilities of process mining.