Duong et al. 2023 — GNN for Remaining Cycle Time Prediction

Applies graph neural networks (GNN) to remaining cycle-time prediction. Argues that traces are naturally graph-structured and that flattening them into sequences (for LSTM) loses structural information.

Contribution

GNN architecture operating directly on the graph representation of an event prefix.
Outperforms LSTM short-term memory baselines, particularly on complex processes where the control flow branches heavily.

Significance in the PPM landscape

Represents the post-Transformer experimentation phase (2022+) in PPM: after LSTMs (2017–2019) and Transformers (2020–2022), researchers began exploring GNNs and hybrid models seeking process-structure-aware inductive biases.

Connections

Concepts: concepts/predictive-process-monitoring · concepts/remaining-time-prediction · concepts/lstm-ppm · concepts/transformer-ppm