SwellDB: Dynamic Query-Driven Table Generation with Large Language Models
Summary: LLM-driven dynamic table generation: SwellDB synthesizes data from LLMs, files, DBs, and text to satisfy prompts and SQL schemas. Mitigates closed-world limits via selective source choice and SQL-ready, queryable tables executed by SwellDB. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Victor Giannakouris (Cornell University)
- 2. Immanuel Trummer (Cornell University)
BibTeX Citation
@inproceedings{giannakouris_sigmod25,
title = {{SwellDB: Dynamic Query-Driven Table Generation with Large Language Models}},
author = {Giannakouris, Victor and Trummer, Immanuel},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3722212.3725136},
url = {https://dl.acm.org/doi/10.1145/3722212.3725136},
year = {2025}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 501 | Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes | 2024 | VLDB | 0.00017267905 |
| 748 | Palimpzest: Optimizing AI-Powered Analytics with Declarative Query Processing | 2025 | CIDR | 0.00014281926 |
| 1,851 | CrowdDB: Query Processing with the VLDB Crowd | 2011 | VLDB | 9.5037355e-05 |
| 4,787 | Hybrid Querying Over Relational Databases and Large Language Models | 2025 | CIDR | 6.4143303e-05 |
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