2024/11/11 by Wilhelm Ågren, Ågren, Wilhelm, Victorio Úbeda Sosa +1
Computer Science · #Databases (cs.DB) #FOS: Computer and information sciences #Image Processing and 3D Reconstruction #Machine Learning (cs.LG) #Speech Recognition and Synthesis #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2411.07009
openalex publication_date 2024/11/11 · openalex created_date 2024/11/15 · openalex updated_date 2026/07/28
The generation of synthetic data is a state-of-the-art approach to leverage when access to real data is limited or privacy regulations limit the usability of sensitive data. A fair amount of research has been conducted on synthetic data generation for single-tabular datasets, but only a limited amount of research has been conducted on multi-tabular datasets with complex table relationships. In this paper we propose the algorithm HCTGAN to synthesize multi-tabular data from complex multi-tabular datasets. We compare our results to the probabilistic model HMA1. Our findings show that our proposed algorithm can more efficiently sample large amounts of synthetic data for deep and complex multi-tabular datasets, whilst achieving adequate data quality and always guaranteeing referential integrity. We conclude that the HCTGAN algorithm is suitable for generating large amounts of synthetic data efficiently for deep multi-tabular datasets with complex relationships. We additionally suggest that the HMA1 model should be used on smaller datasets when emphasis is on data quality.