SyntheRela : a benchmark for synthetic relational database generation

Hudovernik, Valter (author), Jurkovič, Martin (author), Štrumbelj, Erik (author)

Synthesizing relational databases has started to receive more attention from researchers, practitioners, and industry. The task is more difficult than synthesizing a single table due to the added complexity of relationships between tables. For the same reasons, benchmarking methods for synthesizing relational databases introduces new challenges. Our work is motivated by a lack of an empirical evaluation of state-of-the-art methods and by gaps in the understanding of how such an evaluation should be done. We review related work on relational database synthesis, common benchmarking datasets, and approaches to measuring the fidelity and utility of synthetic data. We combine best practices, a novel robust detection metric, and a novel approach to evaluating utility with graph neural networks into a benchmarking tool. We use this benchmark to compare 6 open-source methods over 8 real-world databases, with a total of 39 tables. The open-source SyntheRela benchmark is available on GitHub with a public leaderboard.

• Language: English
• Keywords: relational databases, synthetic data, benchmarking, evaluation
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2026
• Number of pages: 37 str.
• PID: 20.500.12556/RUL-182333
• UDC: 004.65
• ISSN on article: 2835-8856
• COBISS.SI-ID: 276886531
• Publication date in RUL: 07.05.2026

Record is a part of a journal

• Title: Transactions on machine learning research
• Shortened title: Transact. mach. learn. res.
• Publisher: OpenReview.net
• ISSN: 2835-8856
• COBISS.SI-ID: 233590019

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: relacijske baze, sintetični podatki, primerjalna analiza, ocenjevanje