Databases Unbound: Querying All of the World’s Bytes with AI
Summary: Argues that AI-powered semantic extraction can bring unstructured text, images, and video into database querying. Advocates declarative AI data systems to address scalability, correctness, and reliability, illustrated by document/video systems and research challenges. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Samuel Madden (Massachusetts Institute of Technology)
- 2. Michael Cafarella (Massachusetts Institute of Technology)
- 3. Michael Franklin (University of Chicago)
- 4. Tim Kraska (Massachusetts Institute of Technology)
BibTeX Citation
@article{madden_vldb24,
title = {{Databases Unbound: Querying All of the World’s Bytes with AI}},
author = {Madden, Samuel and Cafarella, Michael and Franklin, Michael and Kraska, Tim},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {12},
pages = {4546--4554},
doi = {10.14778/3685800.3685916},
url = {https://doi.org/10.14778/3685800.3685916},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,439 | AOP: Automated and Interactive LLM Pipeline Orchestration for Answering Complex Queries | 2025 | CIDR | 5.6181103e-05 |
| 9,136 | Sphinteract: Resolving Ambiguities in NL2SQL Through User Interaction | 2025 | VLDB | 5.3166292e-05 |
| 9,841 | Semantic Integrity Constraints: Declarative Guardrails for AI-Augmented Data Processing Systems | 2025 | VLDB | 5.2101877e-05 |
| 10,206 | Bridging the Gap: Cardinality Estimation for Semantic Queries on Unstructured Data | 2026 | SIGMOD | 5.093636e-05 |
| 10,433 | Beyond Relational: Semantic-Aware Multi-Modal Analytics with LLM-Native Query Optimization | 2026 | SIGMOD | 5.093636e-05 |
| 10,623 | KEN: An Execution Engine for Unstructured Database Systems | 2026 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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