Interactive Data Cleaning
Interactive Data Cleaning is a data cleaning methodology that distinguishes itself by incorporating iterative human-in-the-loop feedback to guide and refine error detection and correction processes, thereby enabling more accurate and context-sensitive data quality improvements.
16
Publications
1.1K
Citations
56
Authors
33
Institutions
Publications per year
2017–2026
6
Parents
16
Auto-Prep: Efficient and Automated Data Preprocessing Pipeline
Mehwish Bilal, Ghulam Ali, Muhammad Waseem Iqbal et al. · IEEE Access · 2022 · 78 citations · Full text
Interactive and Deterministic Data Cleaning
Jian He, Enzo Veltri, Donatello Santoro et al. · 2016 · 77 citations · Full text
Towards reliable interactive data cleaning
Sanjay Krishnan, Daniel Haas, Michael J. Franklin et al. · 2016 · 56 citations
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33
Tsinghua University
Beijing, China
4 h-index
12 publications
482 citations

City, University of London
London, United Kingdom
2 h-index
2 publications
74 citations
Fraunhofer Institute for Intelligent Analysis and Information Systems
Sankt Augustin, Germany
1 h-index
1 publications
56 citations
Qatar Airways (Qatar)
Doha, Qatar
1 h-index
1 publications
77 citations
Columbia University
New York, United States
1 h-index
1 publications
56 citations
Amazon (United States)
Seattle, United States
1 h-index
1 publications
18 citations
The Alan Turing Institute
London, United Kingdom
1 h-index
4 publications
72 citations

TU Wien
Vienna, Austria
1 h-index
6 publications
258 citations
1–8 of 33
Repository · 1 publications · 24 citations
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1 publications · 56 citations
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1 publications · 19 citations
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PVLDB · 1 publications · 19 citations
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1 publications · 43 citations
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