Windowed Stream Processing

Windowed Stream Processing is a stream processing paradigm that partitions continuous data streams into finite, time- or count-based segments called windows, enabling aggregate computations over bounded subsets of streaming data rather than the entire unbounded stream.

38

Publications

1.9K

Citations

116

Authors

59

Institutions

Publications per year

2017–2026

10

Authors

116

  1. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    3 publications · 127 citations

    3

    h-index

  2. BB
    Brian Babcock

    Stanford University, United States

    2 publications · 347 citations

    2

    h-index

  3. BG
    Buğra Gedik

    IBM (United States), United States

    2 publications · 67 citations

    2

    h-index

  4. MD
    Mayur Datar

    Stanford University, United States

    2 publications · 347 citations

    2

    h-index

  5. TC
    Toon Calders

    University of Antwerp, Belgium

    2 publications · 58 citations

    2

    h-index

1–5 of 116

Venues

  1. Sensors

    1 publications · 55 citations

    1

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  2. Journal of Computer Science and Technology

    1 publications · 47 citations

    1

    h-index

  3. Journal of Information Processing Systems

    1 publications · 37 citations

    1

    h-index

  4. Expert Systems with Applications

    1 publications · 16 citations

    1

    h-index

  5. Enterprise Information Systems

    ELS · 1 publications · 13 citations

    1

    h-index

1–5