Predictive Process Monitoring Using Object-centric Graph Embeddings
This work addresses process prediction challenges in business process management, but it is incremental as it builds on existing methods with a hybrid approach.
The paper tackles predictive process monitoring by proposing an end-to-end model using graph attention networks and LSTMs to predict next activities and event times, achieving competitive performance on real-life and synthetic event logs.
Object-centric predictive process monitoring explores and utilizes object-centric event logs to enhance process predictions. The main challenge lies in extracting relevant information and building effective models. In this paper, we propose an end-to-end model that predicts future process behavior, focusing on two tasks: next activity prediction and next event time. The proposed model employs a graph attention network to encode activities and their relationships, combined with an LSTM network to handle temporal dependencies. Evaluated on one reallife and three synthetic event logs, the model demonstrates competitive performance compared to state-of-the-art methods.