# SchemaDB: Structures in Relational Datasets

arXiv: https://arxiv.org/abs/2111.12835 · HTML: https://mlsanity.com/paper/2111.12835/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 24 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models](https://mlsanity.com/paper/1802.08773/) (2018)
- [Efficient Graph Generation with Graph Recurrent Attention Networks](https://mlsanity.com/paper/1910.00760/) (2019)
- [Learning Deep Generative Models of Graphs](https://mlsanity.com/paper/1803.03324/) (2018)
- [DeepGG: a Deep Graph Generator](https://mlsanity.com/paper/2006.04159/) (2020)

## Built on by (most cited first, 2 of 2)

- [Deception for Cyber Defence: Challenges and Opportunities](https://mlsanity.com/paper/2208.07127/) (2022)
- [WikiDBGraph: A Data Management Benchmark Suite for Collaborative Learning over Database Silos](https://mlsanity.com/paper/2505.16635/) (2025)

## Abstract (arXiv)

In this paper we introduce the SchemaDB data-set; a collection of relational database schemata in both sql and graph formats. Databases are not commonly shared publicly for reasons of privacy and security, so schemata are not available for study. Consequently, an understanding of database structures in the wild is lacking, and most examples found publicly belong to common development frameworks or are derived from textbooks or engine benchmark designs. SchemaDB contains 2,500 samples of relational schemata found in public repositories which we have standardised to MySQL syntax. We provide our gathering and transformation methodology, summary statistics, and structural analysis, and discuss potential downstream research tasks in several domains.