Discovering Object-Centric Causal Nets with Edge Abstraction

Object-centric process mining (OCPM) is an emerging research area that aims to analyze processes involving multiple object types (for instance, orders, items, and deliveries in an order-handling process) with complex intertwined relations captured in a richer format than traditional event logs. The richness of these data, as represented in the Object-Centric Event Log (OCEL) standard, often causes existing discovery algorithms to generate models overloaded with information, exceeding the cognitive limits of users, and reducing their practical usefulness. To address this challenge, we introduce Object-Centric Causal Nets (OCCN) together with an edge-abstraction technique that simplifies the discovered model by merging similar flows across object types. While OCCN provides native support for concurrency and choice, the edge abstraction is essential for reducing visual clutter and producing simpler yet expressive models. A Python implementation is provided, and a comparative evaluation against Object-Centric Petri Nets and Object-Centric Directly-Follows Graphs shows that OCCN with edge abstraction yields models that are easier to understand and more effective in enabling users to identify workflow patterns.