QUEST: Query Optimization in Unstructured Document Analysis
Summary: QUEST optimizes LLM-powered document queries with indexed, evidence-augmented extraction that limits text sent to models. Instance-specific, cost-aware plans reorder operators and turn joins into filters, yielding 30%–6× savings with higher F1. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zhaoze Sun (Beijing Institute of Technology)
- 2. Chengliang Chai (Beijing Institute of Technology)
- 3. Qiyan Deng (Beijing Institute of Technology)
- 4. Kaisen Jin (Beijing Institute of Technology)
- 5. Xinyu Guo (University of Arizona)
- 6. Han Han (University of Arizona)
- 7. Ye Yuan (Beijing Institute of Technology)
- 8. Guoren Wang (Beijing Institute of Technology)
- 9. Lei Cao (University of Arizona)
BibTeX Citation
@article{sun_vldb25,
title = {{QUEST: Query Optimization in Unstructured Document Analysis}},
author = {Sun, Zhaoze and Chai, Chengliang and Deng, Qiyan and Jin, Kaisen and Guo, Xinyu and Han, Han and Yuan, Ye and Wang, Guoren and Cao, Lei},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {11},
pages = {4560--4573},
doi = {10.14778/3749646.3749713},
url = {https://doi.org/10.14778/3749646.3749713},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,190 | AgenticScholar: Agentic Data Management with Pipeline Orchestration for Scholarly Corpora | 2026 | SIGMOD | 5.093636e-05 |
| 10,206 | Bridging the Gap: Cardinality Estimation for Semantic Queries on Unstructured Data | 2026 | SIGMOD | 5.093636e-05 |
| 10,270 | MoDora: Tree-Based Semi-Structured Document Analysis System | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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