Approximate Aggregation

Approximate Aggregation is a data aggregation method that belongs to the class of estimation techniques designed to efficiently compute aggregate query results, distinguished by its deliberate trade-off of precision for reduced computational cost, memory usage, or response time through the use of statistical summaries, sampling, or sketching algorithms.

116

Publications

8.3K

Citations

246

Authors

122

Institutions

Publications per year

2017–2026

18

Authors

246

  1. SC
    Surajit Chaudhuri

    Microsoft (United States), United States

    7 publications · 889 citations

    7

    h-index

  2. SC
    Siyao Cheng

    Harbin Institute of Technology, China

    7 publications · 534 citations

    7

    h-index

  3. GD
    Gautam Das

    The University of Texas at Arlington, United States

    7 publications · 841 citations

    7

    h-index

  4. RB
    Rafael Bravo de la Parra

    Universidad de Alcalá, Spain

    6 publications · 136 citations

    6

    h-index

  5. GK
    George Kollios

    Boston University, United States

    5 publications · 908 citations

    5

    h-index

1–5 of 246

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 8 publications · 370 citations

    7

    h-index

  2. ACM SIGMOD Record

    6 publications · 909 citations

    6

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 4 publications · 122 citations

    4

    h-index

  4. ACM Transactions on Database Systems

    TODS · 3 publications · 322 citations

    3

    h-index

  5. Data Science and Engineering

    2 publications · 141 citations

    2

    h-index

1–5