Distributed Data Processing

Distributed Data Processing is a data processing paradigm that involves the execution of computational tasks across multiple interconnected nodes or systems to manage, transform, and analyze large-scale datasets in parallel, distinguished from centralized approaches by its reliance on partitioned data and coordinated distributed algorithms.

8.5K

Publications

549K

Citations

26.1K

Authors

3.4K

Institutions

Publications per year

2017–2026

2.4K

Authors

26.1K

  1. IS
    Ion Stoica

    University of California, Berkeley, United States

    41 publications · 19.7K citations

    35

    h-index

  2. IF
    Ian Foster

    Argonne National Laboratory, United States

    61 publications · 12.8K citations

    32

    h-index

  3. DK
    Dhabaleswar K. Panda

    The Ohio State University, United States

    46 publications · 2K citations

    24

    h-index

  4. FH
    Francisco Herrera

    Universidad de Granada, Spain

    26 publications · 3.3K citations

    22

    h-index

  5. GF
    Geoffrey Fox

    Indiana University Bloomington, United States

    32 publications · 2.6K citations

    21

    h-index

1–5 of 26.1K

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 198 publications · 18.4K citations

    70

    h-index

  2. arXiv (Cornell University)

    Repository · 272 publications · 27.7K citations

    57

    h-index

  3. Future Generation Computer Systems

    FGCS · 140 publications · 7.4K citations

    44

    h-index

  4. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 101 publications · 5.9K citations

    42

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 59 publications · 2.6K citations

    31

    h-index

1–5