arXiv:2607.03659v1 Announce Type: cross Abstract: 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
The proliferation of deep learning methods for relational databases necessitates a standardized benchmarking effort to assess their true capabilities and foster consistent progress.
This standardization will accelerate development and adoption of AI methods for enterprise data, unlocking new efficiencies and insights from foundational business infrastructure.
The ability to accurately compare deep learning models for relational databases allows for more informed development decisions and helps identify the most effective approaches.
- · Enterprises with large relational databases
- · AI/ML developers
- · Database security and optimization firms
- · Proprietary deep RDB models without rigorous validation
- · Organizations relying on outdated database analytics
Improved performance and reliability of AI models interacting with relational databases.
Increased efficiency and automation in data management and analytics within enterprises.
New business models emerging from advanced, AI-driven insights derived from enterprise relational data.
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Read at arXiv cs.AI