  Predictive Process Monitoring focuses on predicting future states of ongoing
process executions, such as forecasting the remaining time. Recent developments
in Object-Centric Process Mining have enriched event data with objects and
their explicit relations between events. To leverage this enriched data, we
propose the Heterogeneous Object Event Graph encoding (HOEG), which integrates
events and objects into a graph structure with diverse node types. It does so
without aggregating object features, thus creating a more nuanced and
informative representation. We then adopt a heterogeneous Graph Neural Network
architecture, which incorporates these diverse object features in prediction
tasks. We evaluate the performance and scalability of HOEG in predicting
remaining time, benchmarking it against two established graph-based encodings
and two baseline models. Our evaluation uses three Object-Centric Event Logs
(OCELs), including one from a real-life process at a major Dutch financial
institution. The results indicate that HOEG competes well with existing models
and surpasses them when OCELs contain informative object attributes and
event-object interactions.
