Interactive Data Wrangling

Interactive Data Wrangling is a data wrangling paradigm that enables real-time, user-driven manipulation and transformation of data through immediate feedback and iterative exploration, distinguishing itself from batch or automated approaches by emphasizing human-in-the-loop interaction during the data preparation process.

15

Publications

1.6K

Citations

43

Authors

34

Institutions

Publications per year

2017–2026

7

Authors

43

  1. SK
    Sean Kandel

    Stanford University, United States

    3 publications · 1K citations

    3

    h-index

  2. JH
    Jeffrey Heer

    University of Washington, United States

    3 publications · 1K citations

    3

    h-index

  3. JM
    Joseph M. Hellerstein

    University of California, Berkeley, United States

    2 publications · 687 citations

    2

    h-index

  4. SG
    Sumit Gulwani

    Microsoft (United States), United States

    2 publications · 149 citations

    2

    h-index

  5. PJ
    Philip J. Guo

    University of California San Diego, United States

    2 publications · 220 citations

    2

    h-index

1–5 of 43

Venues

  1. Information Visualization

    1 publications · 331 citations

    1

    h-index

  2. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 1 publications · 24 citations

    1

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 1 publications · 18 citations

    1

    h-index

  4. Journal of Statistics Education

    JSE · 1 publications · 26 citations

    1

    h-index

  5. Journal of Coastal Conservation

    1 publications · 10 citations

    1

    h-index

1–5