Hierarchical Analytics

Hierarchical Analytics is a distributed data processing framework that organizes analytical computations across multiple tiers of networked devices, from edge nodes to cloud servers, to optimize latency, bandwidth utilization, and decision-making efficiency by executing progressively refined analysis at each hierarchical level.

14

Publications

2K

Citations

35

Authors

22

Institutions

Publications per year

2017–2026

8

Authors

35

  1. WH
    William Ho

    Aston University, United Kingdom

    1 publications · 478 citations

    1

    h-index

  2. JD
    Jai Dayal

    Georgia Institute of Technology, United States

    1 publications · 15 citations

    1

    h-index

  3. LY
    Liuqing Yang

    Colorado State University, United States

    1 publications · 12 citations

    1

    h-index

  4. CC
    Che‐Wei Chang

    Yuanpei University, Taiwan

    1 publications · 137 citations

    1

    h-index

  5. FZ
    Fang Zheng

    Georgia Institute of Technology, United States

    1 publications · 15 citations

    1

    h-index

1–5 of 35

Venues

  1. IEEE Transactions on Industrial Informatics

    2 publications · 216 citations

    2

    h-index

  2. Journal of Parallel and Distributed Computing

    1 publications · 42 citations

    1

    h-index

  3. IEEE Internet of Things Journal

    1 publications · 12 citations

    1

    h-index

  4. IEEE Computational Intelligence Magazine

    CIM · 1 publications · 191 citations

    1

    h-index

  5. The Journal of Supercomputing

    1 publications · 16 citations

    1

    h-index

1–5