Učenje grafovskih predstavitev za evalvacijo sintetičnih relacijskih podatkov
Year: 2025
Type: dissertation
Abstract: Evaluating the utility of synthetic relational databases is challenging, as existing approaches rely on manual feature engineering or single-table flattening, which obscure relational structure and reduce scalability. This thesis introduces RDL-utility, a general framework that represents relational databases as heterogeneous graphs and trains graph neural networks (GNNs) directly on the graphs. Using a standardized AutoComplete task, RDL-utility measures how well models trained on synthetic data perform on real held-out data. Experiments on five real-world databases, including an ablation study across six GNN architectures, show that diffusion-based generative methods achieve the highest utility, although no single method consistently outperforms all others. RDL-utility provides a reproducible, structure-sensitive evaluation, establishing a foundation for future research and applications.
Author: Jurkovič, Martin
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7083346317
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