This repository is a curated list of academic papers focused on the application of Artificial Intelligence (AI), particularly machine learning (ML) and deep learning, to database systems. It serves researchers, engineers, and practitioners interested in areas like autonomous databases, query optimization, index tuning, and natural language interfaces for databases. The primary benefit is a centralized, categorized, and continuously updated resource for staying abreast of advancements in this rapidly evolving field.

How It Works

The repository functions as a comprehensive bibliography, categorizing papers by their specific application within database systems. This includes sections on system design, configuration tuning, physical design, query optimization, cardinality estimation, and more. The categorization allows users to quickly navigate and find relevant research without sifting through disparate sources. The inclusion of links to source code where available further enhances its utility for practical implementation.

Quick Start & Requirements

This repository is a static list of papers and does not require installation or execution. It serves as a reference guide.

Highlighted Details

• Extensive categorization covering a wide spectrum of ML applications in databases, from core system tuning to advanced topics like Text-to-SQL.
• Includes links to source code for many listed papers, facilitating practical exploration and implementation.
• Actively maintained with new papers being added, reflecting the latest research trends.
• Features a dedicated section for Text-to-SQL, a rapidly growing area within AI for databases.

Maintenance & Community

The repository is maintained by LumingSun and welcomes contributions via pull requests and discussions. It encourages community engagement for supplementing the list and discussing specific topics like Text-to-SQL.

Licensing & Compatibility

The repository itself, as a collection of links and metadata, does not impose a specific license. However, the licensing of the individual papers and their associated source code would be governed by their respective publishers and repositories.

Limitations & Caveats

The README explicitly states that the maintainer is not an expert in all listed topics, particularly Text-to-SQL, and cannot vouch for the quality of all papers. Users are encouraged to contribute and provide feedback on paper quality.