Discovering object-centric Petri nets with parametric arcs

Modeling and Analysis of Information Systems. 2026. Vol. 33. No. 3. P. 394–419.

I.I. Sergeev, I.A. Lomazova

Object-centric process mining has emerged as a powerful paradigm for analyzing event data involving multiple interacting business objects. Existing discovery techniques often rely on object-centric Petri nets with fixed arc multiplicities, limiting their ability to represent parametric resource consumption and production patterns and to capture quantitative dependencies between interacting object types. In this paper, we introduce a discovery approach for object-centric Petri nets with parametric arcs. The proposed method synthesizes models from object-centric event logs while identifying variableweight behavioral patterns. It first discovers control-flow behavior for individual object types and combines the resulting models into an object-centric structure. Subsequently, the numbers of objects participating in events of the same activity are analyzed in order to identify recurring quantitative relations between object types. These relations are represented by constants or linear expressions and are assigned as arc weights in the resulting model. We formalize the underlying model, present the discovery algorithm, and prove that, under the stated assumptions on the discovered per-object-type models, the resulting net can perfectly replay the observed multi-object traces. We further evaluate the applicability of the approach on representative real-world and synthetic object-centric event logs. The evaluation demonstrates that the proposed approach can replace unspecified variable multiplicities with explicit parametric relations while preserving the observed behavior represented by the discovered model. The resulting object-centric Petri nets provide a more compact and expressive representation of object-centric processes and make quantitative synchronization patterns between interacting object types explicit.

Keywords: Petri nets, process discovery, event logs, Object-centric process mining, object-centric Petri nets

Publication based on the results of: New methods in the study of formal computation models, computer systems and related theoretical computer science problems (2025)