Object-centric process mining investigates the intertwined behavior of multiple objects in business processes. From object-centric event logs, object-centric Petri nets (OCPN) can be discovered to replay the behavior of processes accessing different object types. Although they indicate how objects flow through the process and co-occur in events, OCPNs remain underspecified about the relationships of objects. Hence, they are not able to represent synchronization, i.e. executing objects only according to their intended relationships, and fail to identify violating executions. Existing formal modeling approaches, such as object-centric Petri nets with identifiers (OPID), represent object identities and relationships to synchronize them correctly. However, OPID discovery has not yet been studied. This paper uses explicit data models to bridge the gap between OCPNs and formal OPIDs. We identify the implicit assumptions of stable many-to-one relationships in object-centric event logs, which implies synchronization of related objects. To formally underpin this observation, we combine OCPNs with explicit stable many-to-one relationships in a rigorous mapping from OCPNs to OPIDs explicitly capturing the intended stable relationships and the synchronization of related objects. We prove that the original OCPNs and the resulting OPIDs coincide for those executions that satisfy the intended relationships. Moreover, we provide an implementation of the mapping from OCPN to OPID under stable relationships.

Object-centric predictive process monitoring explores and utilizes object-centric event logs to enhance process predictions. The main challenge lies in extracting relevant information and building effective models. In this paper, we propose an end-to-end model that predicts future process behavior, focusing on two tasks: next activity prediction and next event time. The proposed model employs a graph attention network to encode activities and their relationships, combined with an LSTM network to handle temporal dependencies. Evaluated on one real-life and three synthetic event logs, the model demonstrates competitive performance compared to state-of-the-art methods.

Predictive Process Monitoring focuses on predicting future states of ongoing process executions, such as forecasting the remaining time. Recent developments in Object-Centric Process Mining have enriched event data with objects and their explicit relations between events. To leverage this enriched data, we propose the Heterogeneous Object Event Graph encoding (HOEG), which integrates events and objects into a graph structure with diverse node types. It does so without aggregating object features, thus creating a more nuanced and informative representation. We then adopt a heterogeneous Graph Neural Network architecture, which incorporates these diverse object features in prediction tasks. We evaluate the performance and scalability of HOEG in predicting remaining time, benchmarking it against two established graph-based encodings and two baseline models. Our evaluation uses three Object-Centric Event Logs (OCELs), including one from a real-life process at a major Dutch financial institution. The results indicate that HOEG competes well with existing models and surpasses them when OCELs contain informative object attributes and event-object interactions.

Process mining provides various algorithms to analyze process executions based on event data. Process discovery, the most prominent category of process mining techniques, aims to discover process models from event logs, however, it leads to spaghetti models when working with real-life data. Therefore, several clustering techniques have been proposed on top of traditional event logs (i.e., event logs with a single case notion) to reduce the complexity of process models and discover homogeneous subsets of cases. Nevertheless, in real-life processes, particularly in the context of Business-to-Business (B2B) processes, multiple objects are involved in a process. Recently, Object-Centric Event Logs (OCELs) have been introduced to capture the information of such processes, and several process discovery techniques have been developed on top of OCELs. Yet, the output of the proposed discovery techniques on real OCELs leads to more informative but also more complex models. In this paper, we propose a clustering-based approach to cluster similar objects in OCELs to simplify the obtained process models. Using a case study of a real B2B process, we demonstrate that our approach reduces the complexity of the process models and generates coherent subsets of objects which help the end-users gain insights into the process.