Data Provenance Encoding

Data Provenance Encoding is a methodological framework that formally represents the origins, transformations, and dependencies of data throughout its lifecycle to enable traceability and accountability in data quality visualization.

8

Publications

209

Citations

21

Authors

5

Institutions

Publications per year

2017–2026

4

Authors

21

  1. CW
    Changda Wang

    Jiangsu University, China

    4 publications · 115 citations

    4

    h-index

  2. EB
    Elisa Bertino

    Purdue University West Lafayette, United States

    4 publications · 142 citations

    4

    h-index

  3. SS
    Salmin Sultana

    Purdue University West Lafayette, United States

    1 publications · 39 citations

    1

    h-index

  4. RA
    Rizwan Akhtar

    Jiangsu University of Science and Technology, China

    1 publications · 12 citations

    1

    h-index

  5. SA
    Syaiful Alam

    Purdue University West Lafayette, United States

    1 publications · 16 citations

    1

    h-index

1–5 of 21

Venues

  1. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 1 publications · 51 citations

    1

    h-index

  2. Data Science and Engineering

    1 publications · 23 citations

    1

    h-index

  3. Econstor (Econstor)

    Repository · 1 publications · 17 citations

    1

    h-index

  4. ACM Transactions on Sensor Networks

    1 publications · 29 citations

    1

    h-index

1–4