Hash-based Duplicate Detection

Hash-Based Duplicate Detection is a duplicate detection method that uses hash functions to map data items into fixed-length identifiers, enabling efficient comparison and identification of duplicates by leveraging the property that identical items produce identical hash values.

64

Publications

3.8K

Citations

212

Authors

94

Institutions

Publications per year

2017–2026

21

Authors

212

  1. FB
    Frank Breitinger

    University of New Haven, United States

    3 publications · 115 citations

    3

    h-index

  2. HB
    Harald Baier

    Darmstadt University of Applied Sciences, Germany

    3 publications · 115 citations

    3

    h-index

  3. WX
    Wen Xia

    Huazhong University of Science and Technology, China

    2 publications · 404 citations

    2

    h-index

  4. WT
    Warren Toomey

    Bond University, Australia

    2 publications · 29 citations

    2

    h-index

  5. VM
    Vladislav Myasnikov

    Samara National Research University, Russia

    2 publications · 29 citations

    2

    h-index

1–5 of 212

Venues

  1. Digital Investigation

    4 publications · 589 citations

    4

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 87 citations

    2

    h-index

  3. Proceedings of the IEEE

    1 publications · 308 citations

    1

    h-index

  4. 1

    h-index

  5. 1

    h-index

1–5