Object-centric Process Predictive Analytics

TL;DR

This paper introduces a novel approach for object-centric process predictive analytics that leverages interactions between process instances to improve prediction accuracy, validated on real-world data.

Contribution

It presents a new method to incorporate object interactions into predictive models, addressing limitations of previous approaches that ignored these complex relationships.

Findings

Enhanced prediction accuracy using object interaction data

Significant improvement over naive models that ignore interactions

Validated on real-life object-centric process event data

Abstract

Object-centric processes (a.k.a. Artifact-centric processes) are implementations of a paradigm where an instance of one process is not executed in isolation but interacts with other instances of the same or other processes. Interactions take place through bridging events where instances exchange data. Object-centric processes are recently gaining popularity in academia and industry, because their nature is observed in many application scenarios. This poses significant challenges in predictive analytics due to the complex intricacy of the process instances that relate to each other via many-to-many associations. Existing research is unable to directly exploit the benefits of these interactions, thus limiting the prediction quality. This paper proposes an approach to incorporate the information about the object interactions into the predictive models. The approach is assessed on real-life object-centric process event data, using different KPIs. The results are compared with a naive approach that overlooks the object interactions, thus illustrating the benefits of their use on the prediction quality.