Object-centric process mining (OCPM) is gaining traction in both academia and industry due to its ability to model real-world processes more accurately than traditional case-centric approaches. By considering multiple interacting objects, object-centric Petri nets (OCPNs) offer a richer process representation, but this also introduces unique challenges for visualization. The presence of multiple object types, variable arcs, and complex interactions complicates the creation of clear and interpretable layouts. In this paper, we address these challenges by presenting a dedicated layout algorithm tailored to the structural characteristics of OCPNs. Inspired by the Sugiyama framework, the algorithm balances aesthetic and functional criteria, guided by a set of domain-specific quality metrics. We implemented our approach in an open-source web-based tool, OCPN Visualizer, and a reusable JavaScript library for integration into third-party applications. A user study confirms the practical relevance of our approach and highlights its effectiveness in improving the interpretability of object-centric process visualizations.
Despite the recognized importance of business process models, many processes remain unmodeled or inadequately modeled due to the time-consuming nature of manual modeling and the lack of expertise in this field. This paper proposes a novel approach for the automated process model generation, based on object analysis. It utilizes object instances, which are represented in documents and data entities, and which activities create, read, modify and consume during process execution. In contradistinction to conventional approaches that rely on event logs, our object-based approach encompasses both automated and manual activities by analyzing these tangible artifacts such as orders, invoices, and delivery notes that are already documented. In contrast, activities frequently require supplementary tracking mechanisms. The approach extracts process knowledge by identifying object types, assigning object instances to process instances, and deriving activity types from object relationships. The resulting process models are represented as high-level Petri nets, where places represent object types, transitions represent activity types, and arcs capture control and object flow while preserving object structures. Evaluation results show that this method produces more compact models than conventional discovery algorithms and identifies activities and business rules often missed by purely temporal analyses. It enables process discovery even in environments with limited system visibility or significant manual work.
