Human-in-the-loop Data Wrangling
Human-In-The-Loop Data Wrangling is a data wrangling paradigm that integrates human expertise and decision-making directly into the iterative process of cleaning, transforming, and structuring raw data, distinguishing itself from fully automated approaches by leveraging human judgment to resolve ambiguities, validate transformations, and improve data quality under uncertainty.
7
Publications
538
Citations
30
Authors
26
Institutions
Publications per year
2017–2026
5
Variants
Hitl Data Wrangling
Parents
7
Generating and using real-world data: A worthwhile uphill battle
Karin Verkerk, Emile E. Voest · Cell · 2024 · 34 citations · Full text
1–5 of 7
30
1
h-index
1
h-index
1
h-index
1
h-index
1
h-index
1–5 of 30
26
Human Media
Belmont, United States
1 h-index
1 publications
331 citations

University of Oslo
Oslo, Norway
1 h-index
1 publications
18 citations
Institute for Cognitive Science Studies
Tehran, Iran
1 h-index
2 publications
48 citations
The Alan Turing Institute
London, United Kingdom
1 h-index
4 publications
72 citations
California Polytechnic State University
San Luis Obispo, United States
1 h-index
3 publications
114 citations

DePaul University
Chicago, United States
1 h-index
1 publications
46 citations
Michigan State University
East Lansing, United States
1 h-index
1 publications
46 citations
University of Oxford
Oxford, United Kingdom
1 h-index
1 publications
18 citations
1–8 of 26
1 publications · 34 citations
1
h-index
DSJIE · 1 publications · 38 citations
1
h-index
1 publications · 331 citations
1
h-index
ORM · 1 publications · 46 citations
1
h-index
TKDE · 1 publications · 18 citations
1
h-index
1–5