CICERONE: A natural language-based global approach for object-centric Predictive Process Monitoring

The object-centric paradigm has recently transformed research in Predictive Process Monitoring (PPM) thanks to new formalisms and techniques to handle ongoing process executions as interconnected and mutually influencing execution traces. Hence, the predictive objective of PPM methods has passed from delivering a prediction for a single object to delivering a prediction for all ongoing objects simultaneously. Building on this renewed goal of object-centric PPM, we introduce a natural language flow process representation to obtain the story telling of ongoing trace executions by preserving relationships between multiple objects involved in the same process execution, as well as relationships among the ongoing trace executions sharing the same objects. Based upon these representations, we introduce CICERONE a novel object-centric PPM method that uses a Large Language Model coupled with a global learning approach to deliver predictions for all objects involved in all ongoing process executions in a single step. Experiments across some Object-Centric Event Logs (OCELs) assess the effectiveness of CICERONE compared to competing object-centric PPM methods. In addition, we perform an occlusion-based analysis to examine the effect of object interactions regarding ongoing process executions on all the delivered predictions.