Why Diffusion Models Must Be the Next Great Step in Relational Data Synthesis(09/23)

by Admin_Azoo 23 Sep 2024

Why Diffusion Models Must Be the Next Great Step in Relational Data Synthesis(09/23)

Relational data synthesis is much more challenging than synthesizing single-table data because it involves handling complex relationships between multiple tables. In real-world scenarios, data is often divided across several tables that are linked by relationships such as foreign keys. This means that preserving the tight connections between tables and considering the long-range dependencies between different attributes is crucial. However, traditional data synthesis methods mostly focus on single-table data, and they struggle to properly reflect these relationships in multi-table structures or face scalability issues when dealing with large datasets.

Diffusion Models: A New Possibility

Recently, diffusion models have gained attention in fields like image generation, where they progressively transform data to generate new samples. These models excel at exploring latent spaces and learning complex data distributions, making them highly effective for generating new data. Thanks to these capabilities, diffusion models hold great potential for relational data synthesis as well. In particular, they can be used to learn the relationships between tables and generate data based on these learned relationships.

Potential of Diffusion Models in Relational Data Synthesis

When applied to relational data synthesis, diffusion models can serve as a powerful tool for modeling complex structural properties such as table relationships and foreign key constraints. For example, by leveraging clustering labels or latent variables, diffusion models can effectively capture long-range dependencies between tables. Unlike traditional methods that handle tables independently, diffusion models take into account how different tables are connected, allowing for more refined data synthesis.

Research on applying diffusion models to relational data synthesis is still in its early stages, but the potential is immense. Diffusion models are emerging as a promising solution to overcome the limitations of existing multi-table synthesis techniques. They are expected to enable data synthesis that is both more scalable and capable of producing higher utility. As diffusion models continue to evolve, we can expect exciting advancements in the field of relational data synthesis.