A Fair Benchmarking of Deep Relational Database Learning Models
Relational databases (RDBs) are the primary data infrastructure in many enterprises, yet recent deep learning methods designed for RDBs have been evaluated under inconsistent experimental protocols, making fair comparison difficult. We present one of the first systematic benchmarking studies of recently released deep learning methods for RDBs, evaluating them across five relational databases, with one classification and one regression task for each. We refactor all deep RDB models to allow the full range of experimental procedures to be applied consistently across all methods. Our findings indicate that the relational transformer (RT) approach delivers the strongest overall performance on...
| Authors | Kazi F. Akhter, Bharath Ajendla, Manar D. Samad |
|---|---|
| Primary category | cs.DB |
| Published | 4 July 2026 |
| Last updated at source | 4 July 2026 |
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