Stream Processing

Stream Processing is a data processing paradigm within data engineering and big data analytics that involves the continuous, real-time computation and analysis of unbounded data streams as they are generated, distinguishing it from batch processing by its low-latency, event-driven execution model.

7.1K

Publications

361.9K

Citations

16K

Authors

2.6K

Institutions

Publications per year

2017–2026

2.3K

Authors

16K

  1. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    60 publications · 4K citations

    35

    h-index

  2. GC
    Graham Cormode

    AT&T (United States), United States

    38 publications · 5.6K citations

    31

    h-index

  3. AB
    Albert Bifet

    University of Waikato, New Zealand

    47 publications · 4.1K citations

    28

    h-index

  4. JG
    João Gama

    Universidade do Porto, Portugal

    37 publications · 4.3K citations

    25

    h-index

  5. SM
    S. Muthukrishnan

    AT&T (United States), United States

    29 publications · 5.1K citations

    24

    h-index

1–5 of 16K

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 139 publications · 9.6K citations

    54

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 93 publications · 7K citations

    39

    h-index

  3. IEEE Transactions on Multimedia

    88 publications · 3.9K citations

    36

    h-index

  4. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 58 publications · 2.7K citations

    31

    h-index

  5. Information Sciences

    60 publications · 3.3K citations

    29

    h-index

1–5