IterClean: An Iterative Data Cleaning Framework with Large Language Models

Author:

Ni Wei1ORCID,Zhang Kaihang2ORCID,Miao Xiaoye2ORCID,Zhao Xiangyu3ORCID,Wu Yangyang2ORCID,Yin Jianwei2ORCID

Affiliation:

1. ZHEJIANG UNIVERSITY, CN and CITY UNIVERSITY of HONG KONG, Hong Kong

2. ZHEJIANG UNIVERSITY, CN

3. CITY UNIVERSITY of HONG KONG, HK

Funder

Henan Institute of Chinese Engineering Development Strategies

Research Impact Fund

APRC-CityU New Research Initiatives

Beijing Life Science Academy

CityU-HKIDS Early Career Research Grant

Fundamental Research Funds for the Central Universities

Publisher

ACM

Reference46 articles.

1. Mohamed Abdelaal, Christian Hammacher, and Harald Schöning. 2023. REIN: A comprehensive benchmark framework for data cleaning methods in ML pipelines. In EDBT. 499–511.

2. Serge Abiteboul, Richard Hull, and Victor Vianu. 1995. Foundations of Databases: The Logical Level. Addison-Wesley Longman Publishing Cooperation, Inc.

3. Laure Berti-Equille Tamraparni Dasu and Divesh Srivastava. 2011. Discovery of complex glitch patterns: A novel approach to quantitative data cleaning. In ICDE. 733–744.

4. Sampling the repairs of functional dependency violations under hard constraints

5. BigDaMa. 2023. Error generator. https://github.com/BigDaMa/error-generator.

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