Object-centric Process Predictive Analytics
This work addresses predictive analytics challenges for industries using object-centric processes, but it is incremental as it builds on existing methods by incorporating interaction information.
The paper tackles the challenge of predictive analytics in object-centric processes, where process instances interact through many-to-many associations, by proposing an approach that incorporates object interaction information into predictive models. The results show improved prediction quality compared to a naive approach that overlooks these interactions, as assessed on real-life event data using different KPIs.
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.