Distributed Provenance

Distributed Provenance is a type of data provenance that captures and represents the origin, transformation history, and movement of data across multiple autonomous and geographically dispersed systems, distinguished by its emphasis on interoperability, partial observability, and decentralized trust in heterogeneous environments.

68

Publications

2.4K

Citations

268

Authors

119

Institutions

Publications per year

2017–2026

22

Authors

268

  1. BT
    Boon Thau Loo

    University of Pennsylvania, United States

    7 publications · 175 citations

    7

    h-index

  2. WZ
    Wenchao Zhou

    Georgetown University, United States

    7 publications · 175 citations

    7

    h-index

  3. MM
    Marta Mattoso

    Universidade Federal do Rio de Janeiro, Brazil

    5 publications · 86 citations

    5

    h-index

  4. ZG
    Zachary G. Ives

    University of Pennsylvania, United States

    5 publications · 130 citations

    5

    h-index

  5. AH
    Andreas Haeberlen

    California University of Pennsylvania, United States

    4 publications · 97 citations

    4

    h-index

1–5 of 268

Venues

  1. ACM Transactions on Database Systems

    TODS · 2 publications · 57 citations

    2

    h-index

  2. IEEE Internet of Things Journal

    2 publications · 88 citations

    2

    h-index

  3. Theoretical Chemistry Accounts

    1 publications · 138 citations

    1

    h-index

  4. Computing in Science & Engineering

    1 publications · 17 citations

    1

    h-index

  5. Computers & Security

    1 publications · 14 citations

    1

    h-index

1–5