Stream Processing Pipelines

Stream Processing Pipelines is a computational framework that enables continuous, real-time data processing through a series of ordered, loosely coupled stages, each performing incremental transformations on data streams, thereby distinguishing it from batch-oriented or discrete-task pipelines by its inherent support for unbounded, time-sensitive data flows.

120

Publications

25K

Citations

442

Authors

168

Institutions

Publications per year

2017–2026

45

Authors

442

  1. BG
    Buğra Gedik

    IBM (United States), United States

    5 publications · 221 citations

    5

    h-index

  2. NT
    Nesime Tatbul

    Intel (United States), United States

    5 publications · 118 citations

    5

    h-index

  3. SA
    Saman Amarasinghe

    Massachusetts Institute of Technology, United States

    4 publications · 655 citations

    4

    h-index

  4. WT
    William Thies

    Microsoft Research (India), India

    4 publications · 655 citations

    4

    h-index

  5. MI
    Michael I. Gordon

    Massachusetts Institute of Technology, United States

    4 publications · 655 citations

    4

    h-index

1–5 of 442

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 5 publications · 402 citations

    5

    h-index

  2. Journal of Parallel and Distributed Computing

    3 publications · 43 citations

    3

    h-index

  3. ACM SIGPLAN Notices

    3 publications · 65 citations

    3

    h-index

  4. Applied Sciences

    2 publications · 90 citations

    2

    h-index

  5. The VLDB Journal

    2 publications · 113 citations

    2

    h-index

1–5