Time and Relations into Focus: Ontological Foundations of Object-Centric Event Data

TL;DR

This paper introduces a foundational ontological framework for object-centric event data, enhancing expressiveness and resolving ambiguities in existing models by grounding them in a well-founded ontology.

Contribution

It proposes the gOCED metamodel, extending current models with ontological foundations to better represent time and dynamic relations in object-centric process mining.

Findings

gOCED covers existing metamodel features while adding expressiveness.

Enhanced representation of time and relations in event data.

Addresses ambiguity issues in current OCED models.

Abstract

Object-centric process mining is a new branch of process mining where events are associated with multiple objects, and where object-to-object interactions are essential to understand the process dynamics. Traditional event data models, also called case-centric, are unable to cope with the complexity introduced by these more refined relationships. Several models have been made to move from case-centric to Object-Centric Event Data (OCED), trying to retain simplicity as much as possible. Still, these suffer from inherent ambiguities, and lack a comprehensive support of essential dimensions related to time and (dynamic) relations. In this work, we propose to fill this gap by leveraging a well-founded ontology of events and bringing ontological foundations to OCED, with a three-step approach. First, we start from key open issues reported in the literature regarding current OCED metamodels, and witness their ambiguity and expressiveness limitations on illustrative and representative examples proposed therein. Second, we consider the OCED Core Model, currently proposed as the basis for defining a new standard for object-centric event data, and we enhance it by grounding it on a lightweight version of UFO-B called gUFO, a well-known foundational ontology tailored to the representation of objects, events, time, and their (dynamic) relations. This results in a new metamodel, which we call gOCED. The third contribution then shows how gOCED at once covers the features of existing metamodels preserving their simplicity, and extends them with the essential features needed to overcome the ambiguity and expressiveness issues reported in the literature.