Approximate Stream Processing

Approximate Stream Processing is a stream processing paradigm that trades exactness for efficiency by leveraging approximation techniques to deliver timely, in-memory analysis of high-velocity data streams under resource constraints.

34

Publications

2.3K

Citations

107

Authors

61

Institutions

Publications per year

2017–2026

8

Authors

107

  1. SM
    S. Muthukrishnan

    AT&T (United States), United States

    2 publications · 175 citations

    2

    h-index

  2. RC
    Ruichuan Chen

    Max Planck Institute for Software Systems, Germany

    2 publications · 136 citations

    2

    h-index

  3. TY
    Tong Yang

    Peking University, China

    2 publications · 212 citations

    2

    h-index

  4. JW
    Jennifer Widom

    Stanford University, United States

    2 publications · 753 citations

    2

    h-index

  5. WW
    Wei Wang

    University of California, Los Angeles, United States

    2 publications · 61 citations

    2

    h-index

1–5 of 107

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 2 publications · 92 citations

    2

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 1 publications · 14 citations

    1

    h-index

  3. Discrete Mathematics & Theoretical Computer Science

    DMTCS · 1 publications · 12 citations

    1

    h-index

  4. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)

    Repository · 1 publications · 17 citations

    1

    h-index

  5. Intelligent Data Analysis

    IDA · 1 publications · 25 citations

    1

    h-index

1–5