Probabilistic Data Structures

Probabilistic Data Structures is a class of data organization methods that trade exactness for efficiency by employing randomized algorithms to approximate query responses, enabling sublinear space and time complexity in large-scale data processing.

157

Publications

9.8K

Citations

387

Authors

189

Institutions

Publications per year

2017–2026

48

Variants

Probabilistic Data Structure

Authors

387

  1. TY
    Tong Yang

    Peking University, China

    9 publications · 390 citations

    9

    h-index

  2. PS
    Pierre Senellart

    Télécom Paris, France

    5 publications · 200 citations

    5

    h-index

  3. DS
    Dan Suciu

    University of Washington, United States

    5 publications · 1.5K citations

    5

    h-index

  4. SB
    Shalini Batra

    Thapar Institute of Engineering & Technology, India

    4 publications · 274 citations

    4

    h-index

  5. XL
    Xiaoming Li

    Peking University, China

    4 publications · 177 citations

    4

    h-index

1–5 of 387

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 7 publications · 477 citations

    7

    h-index

  2. arXiv (Cornell University)

    Repository · 6 publications · 247 citations

    6

    h-index

  3. The VLDB Journal

    6 publications · 1.3K citations

    6

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 4 publications · 185 citations

    4

    h-index

  5. IEEE/ACM Transactions on Networking

    TON · 3 publications · 91 citations

    3

    h-index

1–5