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

Authors

56

  1. NT
    Nan Tang

    Tsinghua University, China

    3 publications · 131 citations

    3

    h-index

  2. GL
    Guoliang Li

    Tsinghua University, China

    3 publications · 131 citations

    3

    h-index

  3. EW
    Eugene Wu

    Columbia University, United States

    2 publications · 80 citations

    2

    h-index

  4. XQ
    Xuedi Qin

    Tsinghua University, China

    2 publications · 54 citations

    2

    h-index

  5. MJ
    Michael J. Franklin

    University of California, Berkeley, United States

    2 publications · 80 citations

    2

    h-index

1–5 of 56

Venues

  1. arXiv (Cornell University)

    Repository · 1 publications · 24 citations

    1

    h-index

  2. Visual Informatics

    1 publications · 56 citations

    1

    h-index

  3. Procedia Computer Science

    1 publications · 19 citations

    1

    h-index

  4. Proceedings of the VLDB Endowment

    PVLDB · 1 publications · 19 citations

    1

    h-index

  5. 2012 Dallas, Texas, July 29 - August 1, 2012

    1 publications · 43 citations

    1

    h-index

1–5