Skyline query processing for large-scale and incomplete graphs using graph convolutional network (GCN)

Abstract

Skyline query processing is essential in multi-criteria decision-making, as it retrieves optimal results without requiring user-defined weights. Traditional skyline methods, however, face significant challenges when applied to large-scale and incomplete datasets. This study proposes a hybrid approach that integrates the ISkyline dominance graph technique with Graph Neural Networks (GNNs), specifically a Graph Convolutional Network (GCN) to improve skyline query performance under such conditions. The GCN component is utilized to predict skyline tuples in the presence of missing or incomplete data. The ISkyline algorithm serves as the foundation for identifying initial dominance relationships and labelling skyline points, enabling the GCN to learn Pareto-optimal patterns from partially incomplete data. Evaluation on both synthetic and real-world datasets demonstrates enhanced accuracy and efficiency when compared to established methods such as ISkyline, SIDS, and OIS. The proposed GNN + ISkyline framework improved classification accuracy by 72%, the F1-score by 71%, and the AUC-ROC by 49% compared to the standalone ISkyline algorithm when evaluated on the CoIL 2000 dataset. This work demonstrates the potential of creating a more efficient query processing, supporting applications in e-commerce, finance, and smart data systems, while aligning with the 9th Sustainable Development Goal on industry, innovation, and infrastructure.