Synthetic Relational

Synthetic relational data generation focuses on creating realistic artificial datasets that mirror the structure and statistical properties of real relational databases, addressing privacy concerns and data scarcity. Current research emphasizes developing advanced generative models, including GANs, variational autoencoders, and transformer-based architectures, to accurately capture complex relationships between multiple tables while maintaining data fidelity and utility for downstream tasks. This field is crucial for enabling data sharing in sensitive domains and improving the performance of machine learning models trained on relational data, particularly in scenarios where obtaining or using real data is impractical or ethically problematic.