Data Dependency Mapping

Data Dependency Mapping is a methodological framework within the field of data engineering that systematically identifies, classifies, and visualizes the causal, structural, and temporal relationships among data elements across systems, processes, or applications to ensure consistency, traceability, and informed decision-making in data-intensive environments.

78

Publications

2.2K

Citations

259

Authors

93

Institutions

Publications per year

2017–2026

13

Authors

259

  1. BP
    Brajendra Panda

    University of Arkansas at Fayetteville, United States

    3 publications · 190 citations

    3

    h-index

  2. MS
    Ming‐Der Shieh

    National Cheng Kung University, Taiwan

    2 publications · 102 citations

    2

    h-index

  3. WL
    Wen-Ching Lin

    National Cheng Kung University, Taiwan

    2 publications · 102 citations

    2

    h-index

  4. XZ
    Xiaodong Zhang

    Tianjin University, China

    1 publications · 43 citations

    1

    h-index

  5. AK
    Adrian Korodi

    Polytechnic University of Timişoara, Romania

    1 publications · 15 citations

    1

    h-index

1–5 of 259

Venues

  1. 2

    h-index

  2. The Journal of Logic Programming

    2 publications · 33 citations

    2

    h-index

  3. Journal of Systems and Software

    JSS · 2 publications · 36 citations

    2

    h-index

  4. ETRI Journal

    1 publications · 47 citations

    1

    h-index

  5. ACM Transactions on Graphics

    TOG · 1 publications · 20 citations

    1

    h-index

1–5