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RetClean: Retrieval-Based Data Cleaning Using LLMs and Data Lakes

Summary: RetClean explores LLM-assisted data cleaning across public world-knowledge, RAG over indexed enterprise data lakes, and privacy-preserving local models fine-tuned with few examples. Its GUI exposes model/accuracy/privacy trade-offs across these scenarios. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13861
Venue
VLDB
Year
2024
Pagerank
6.123486e-05
Overall Rank
5,682 | 61.02%
DOI
10.14778/3685800.3685890

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{naeem_vldb24,
        title = {{RetClean: Retrieval-Based Data Cleaning Using LLMs and Data Lakes}},
        author = {Naeem, Zan Ahmad and Ahmad, Mohammad Shahmeer and Eltabakh, Mohamed and Ouzzani, Mourad and Tang, Nan},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {12},
        pages = {4421--4424},
        doi = {10.14778/3685800.3685890},
        url = {https://doi.org/10.14778/3685800.3685890},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

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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.

Rank Cited Paper Year Venue Pagerank
420 Can Foundation Models Wrangle Your Data? 2023 VLDB 0.00018789852
1,550 Symphony: Towards Natural Language Query Answering over Multi-modal Data Lakes 2023 CIDR 0.00010385904
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