1. Introduction Synthetic data generation is increasingly important when working with sensitive or regulated datasets. While generating synthetic data for single tables is straightforward using GANs or statistical models, generating relational multi-table synthetic data is significantly more complex. Relational databases do not exist in isolation. They contain relationships that define how information flows across the system: Foreign keys (parent → child) Many-to-one (Student → Fees)...

In the realm of data management, integrating information from diverse sources poses significant challenges due to variations in terminology, structure, and content. Traditional matching methods, which depend on exact or approximate string comparisons, often fail to capture underlying meanings, leading to incomplete or inaccurate alignments. To overcome this, fuzzy logic and phonetic matching became prominent approaches. Fuzzy matching uses algorithms like Levenshtein distance or Jaro-Winkler...