QuWARTS: Query Workload Aware Relational Table Synthesis from Unstructured Text
Summary: QuWARTS synthesizes relational tables from unstructured text offline, guided by historical query workloads. It aligns schemas, enforces joinability, and normalizes entities, improving analytical query accuracy while avoiding per-query extraction latency. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Aritra Mazumder (University of Utah)
- 2. Whanhee Cho (University of Utah)
- 3. Anna Fariha (University of Utah)
BibTeX Citation
@article{mazumder_vldb26,
title = {{QuWARTS: Query Workload Aware Relational Table Synthesis from Unstructured Text}},
author = {Mazumder, Aritra and Cho, Whanhee and Fariha, Anna},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4530--4533},
doi = {10.14778/3827998.3828058},
url = {https://doi.org/10.14778/3827998.3828058},
year = {2026}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
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 |
| 683 | DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing | 2025 | VLDB | 0.00014817539 |
| 748 | Palimpzest: Optimizing AI-Powered Analytics with Declarative Query Processing | 2025 | CIDR | 0.00014281926 |
| 4,731 | QUEST: Query Optimization in Unstructured Document Analysis | 2025 | VLDB | 6.4462032e-05 |
| 8,679 | Relational Deep Dive: Error-Aware Queries Over Unstructured Data | 2026 | VLDB | 5.2905577e-05 |
| 8,680 | Unstructured Data Analysis Using LLMs: A Comprehensive Benchmark | 2026 | VLDB | 5.2905577e-05 |
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