Probabilistic Data Structure Design

Probabilistic Data Structure Design is a methodological framework in computer science that employs probabilistic techniques to determine structural properties and performance guarantees in data organization, distinguishing itself from deterministic approaches by leveraging randomness in the construction and maintenance of data structures to achieve efficient average-case time and space complexity.

57

Publications

2.9K

Citations

243

Authors

120

Institutions

Publications per year

2017–2026

21

Parents

Authors

243

  1. TY
    Tong Yang

    Peking University, China

    6 publications · 213 citations

    6

    h-index

  2. JP
    Jason Pell

    Michigan State University, United States

    4 publications · 818 citations

    4

    h-index

  3. CT
    C. Titus Brown

    University of California, Davis, United States

    4 publications · 818 citations

    4

    h-index

  4. XL
    Xiaoming Li

    Peking University, China

    4 publications · 177 citations

    4

    h-index

  5. MS
    Muhammad Shahzad

    North Carolina State University, United States

    3 publications · 155 citations

    3

    h-index

1–5 of 243

Venues

  1. IEEE/ACM Transactions on Networking

    TON · 2 publications · 75 citations

    2

    h-index

  2. ACM Transactions on Algorithms

    TALG · 2 publications · 57 citations

    2

    h-index

  3. Symposium on Discrete Algorithms

    Conference · 2 publications · 42 citations

    2

    h-index

  4. 1

    h-index

  5. Information Fusion

    1 publications · 26 citations

    1

    h-index

1–5