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)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
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}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 713 | Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes | 2024 | VLDB | 0.00014672521 |
| 1,245 | Palimpzest: Optimizing AI-Powered Analytics with Declarative Query Processing | 2025 | CIDR | 0.00011507415 |
| 1,810 | CrowdDB: Query Processing with the VLDB Crowd | 2011 | VLDB | 9.7021022e-05 |
| 4,944 | Hybrid Querying Over Relational Databases and Large Language Models | 2025 | CIDR | 6.432467e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 5,323 | SNAILS: Schema Naming Assessments for Improved LLM-Based SQL Inference | 2025 | SIGMOD |
| 2 | 11,014 | GalaxyWeaver: Autonomous Table-to-Graph Conversion and Schema Optimization with Large Language Models | 2025 | VLDB |
| 3 | 9,542 | Database as Runtime: Compiling LLMs to SQL for In-database Model Serving | 2025 | SIGMOD |
| 4 | 13,339 | DocDB: A Database for Unstructured Document Analysis | 2025 | VLDB |
| 5 | 10,556 | OpenSQL: Data-Efficient Text-to-SQL for Open-Source LLMs via Synthesized Intermediate Supervision | 2026 | VLDB |
| 6 | 10,572 | Relational Deep Dive: Error-Aware Queries Over Unstructured Data | 2026 | VLDB |
| 7 | 11,120 | Welding Natural Language Queries to Analytics IRs with LLMs | 2024 | CIDR |
| 8 | 279 | Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation | 2024 | VLDB |
| 9 | 9,391 | Demonstration of DB-GPT: Next Generation Data Interaction System Empowered by Large Language Models | 2024 | VLDB |
| 10 | 4,944 | Hybrid Querying Over Relational Databases and Large Language Models | 2025 | CIDR |