Stateful Stream Processing

Stateful Stream Processing is a stream processing paradigm that maintains and updates application state across consecutive data events, enabling computations to depend on historical data and context beyond individual events.

47

Publications

2.9K

Citations

145

Authors

72

Institutions

Publications per year

2017–2026

21

Authors

145

  1. KW
    Kun‐Lung Wu

    IBM (United States), United States

    3 publications · 113 citations

    3

    h-index

  2. PP
    Peter Pietzuch

    Imperial College London, United Kingdom

    3 publications · 410 citations

    3

    h-index

  3. PC
    Paris Carbone

    KTH Royal Institute of Technology, Sweden

    3 publications · 180 citations

    3

    h-index

  4. RC
    Raul Castro Fernandez

    University of Chicago, United States

    3 publications · 410 citations

    3

    h-index

  5. VM
    Volker Markl

    Technische Universität Berlin, Germany

    2 publications · 127 citations

    3

    h-index

1–5 of 145

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 4 publications · 479 citations

    4

    h-index

  2. The VLDB Journal

    3 publications · 298 citations

    3

    h-index

  3. arXiv (Cornell University)

    Repository · 2 publications · 74 citations

    2

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 1 publications · 312 citations

    1

    h-index

  5. IEEE Data(base) Engineering Bulletin

    1 publications · 12 citations

    1

    h-index

1–5