Overview

Relational databases organize information into tables connected by keys, with SQL and integrity rules supporting reliable storage, updates, and analysis. Research improves how systems plan and tune queries, protect sensitive data, and integrate or document schemas; newer work also uses language models and agents to translate questions into SQL and explore databases. For learning from relational data, approaches include graph neural networks, relational transformers, and synthetic pretraining, evaluated on tasks such as RelBench as well as SQL benchmarks like Spider and BIRD. A central challenge is making systems dependable across large, unfamiliar schemas and SQL dialects while balancing accuracy, execution cost, privacy, and the complexity of specialized methods.

Latest papers

CardsList

80 papers5 papers in the last four weeks, down 50% on the four weeks before. 0.0% of all new papers.

  1. Database Querying under Missing Values Governed by Missingness Mechanisms

    Apr 7, 2026Leopoldo Bertossi, Farouk Toumani, Maxime BuronRelational DatabasesIncomplete Data Imputation

  2. Automating Database-Native Function Code Synthesis with LLMs

    Apr 2, 2026Wei Zhou, Xuanhe Zhou, Qikang He +4LLM-Based Program SynthesisLLM Code Generation

  3. WAter: A Workload-Adaptive Knob Tuning System based on Workload Compression

    Mar 28, 2026Yibo Wang, Jiale Lao, Chen Zhang +3Database SystemsRelational Databases

  4. Are LLMs Overkill for Databases?: A Study on the Finiteness of SQL

    Mar 26, 2026Yue Li, David Mimno, Unso Eun Seo JoText-to-SQLQuery Complexity

  5. LLM-Based Test Case Generation in DBMS through Monte Carlo Tree Search

    Mar 23, 2026Yujia Chen, Yingli Zhou, Fangyuan Zhang +1Automated Test GenerationRelational Databases

  6. DOT: Dynamic Knob Selection and Online Sampling for Automated Database Tuning

    Mar 16, 2026Yifan Wang, Debabrota Basu, Pierre Bourhis +2Database SystemsRelational Databases

  7. Rel-MOSS: Towards Imbalanced Relational Deep Learning on Relational Databases

    Mar 9, 2026Jun Yin, Peng Huo, Bangguo Zhu +4Relational Deep LearningClass-Imbalanced Learning

  8. Dial: A Knowledge-Grounded Dialect-Specific NL2SQL System

    Mar 8, 2026Xiang Zhang, Hongming Xu, Le Zhou +8Execution-Guided Text-to-SQLText-to-SQL

  9. Fine-Grained Table Retrieval Through the Lens of Complex Queries

    Mar 7, 2026Wojciech Kosiuk, Xingyu Ji, Yeounoh Chung +2Table RetrievalInformation Retrieval

  10. Relational In-Context Learning via Synthetic Pre-training with Structural Prior

    Mar 4, 2026Yanbo Wang, Jiaxuan You, Chuan Shi +1Relational ICLRelational Foundation Models

  11. Towards Effective Orchestration of AI x DB Workloads

    Mar 4, 2026Naili Xing, Haotian Gao, Zhanhao Zhao +6Relational Databases

  12. DPSQL+: A Differentially Private SQL Library with a Minimum Frequency Rule

    Feb 26, 2026Tomoya Matsumoto, Shokichi Takakura, Shun Takagi +1Differential PrivacyRelational Databases

  13. Relatron: Automating Relational Machine Learning over Relational Databases

    Feb 26, 2026Zhikai Chen, Han Xie, Jian Zhang +3Relational Deep LearningModel Selection

  14. Qute: Towards Quantum-Native Database

    Feb 16, 2026Muzhi Chen, Xuanhe Zhou, Wei Zhou +7Relational DatabasesQuantum Circuit Compilation

  15. PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models

    Feb 3, 2026Vignesh Kothapalli, Rishabh Ranjan, Valter Hudovernik +4Relational Foundation ModelsSynthetic Data Pretraining

  16. MetaJoin: Adaptive Join Selections for Heterogeneous Tables

    Dec 2, 2025Ning Wang, Sainyam GalhotraRelational DatabasesEntity Resolution

  17. Evidence-Guided Schema Normalization for Temporal Tabular Reasoning

    Nov 29, 2025Ashish Thanga, Vibhu Dixit, Abhilash Shankarampeta +1KG ConstructionTable QA

  18. Beyond Interestingness: Semantic and Context-Aware Natural Language Query Recommendations for Visual Data Analysis

    Jan 13, 2022Xingbo Wang, Siyuan Li, Furui Cheng +5Visual AnalyticsRecommender Systems