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DocDB: A Database for Unstructured Document Analysis

Summary: DocDB targets LLM extraction as the bottleneck in SQL-like document analytics. Its two-level relevance index and document-adaptive plans selectively process text and minimize costly, heterogeneous extraction calls. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
h8a7322f79834d50b
Venue
VLDB
Year
2025
Pagerank
5.2985603e-05
Overall Rank
8,632 | 41.97%
DOI
10.14778/3750601.3750678

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{li_vldb25,
        title = {{DocDB: A Database for Unstructured Document Analysis}},
        author = {Li, Zequn and Zhong, Yuanhao and Chai, Chengliang and Sun, Zhaoze and Deng, Yuhao and Yuan, Ye and Wang, Guoren and Cao, Lei},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {12},
        pages = {5387--5390},
        doi = {10.14778/3750601.3750678},
        url = {https://doi.org/10.14778/3750601.3750678},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
6,886 Multi-Objective Agentic Rewrites for Unstructured Data Processing 2026 VLDB 5.6551172e-05
10,804 Document-to-Database: Extraction Meets Relational Semantics 2026 VLDB 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
748 Palimpzest: Optimizing AI-Powered Analytics with Declarative Query Processing 2025 CIDR 0.00014281926
Previous Page 1 / 1 Next

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