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

Authors

30

  1. DB
    Dominique Brodbeck

    Union Bank of Switzerland, Switzerland

    1 publications · 331 citations

    1

    h-index

  2. MT
    Michael T. Braun

    Michigan State University, United States

    1 publications · 46 citations

    1

    h-index

  3. KT
    Khoa Thi-Minh Tran

    Dongguk University, South Korea

    1 publications · 24 citations

    1

    h-index

  4. PB
    Paolo Buono

    University of Bari Aldo Moro, Italy

    1 publications · 331 citations

    1

    h-index

  5. FP
    Fred Popowich

    Simon Fraser University, Canada

    1 publications · 24 citations

    1

    h-index

1–5 of 30

Venues

  1. Cell

    1 publications · 34 citations

    1

    h-index

  2. Decision Sciences Journal of Innovative Education

    DSJIE · 1 publications · 38 citations

    1

    h-index

  3. Information Visualization

    1 publications · 331 citations

    1

    h-index

  4. Organizational Research Methods

    ORM · 1 publications · 46 citations

    1

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 1 publications · 18 citations

    1

    h-index

1–5