Top-k Query Optimization

Top-K Query Optimization is a query optimization technique that belongs to the class of methods aimed at minimizing computational cost for retrieving the k highest-ranked results according to a specified scoring function, distinguished by its focus on partial result set generation and early termination strategies to avoid full dataset evaluation.

134

Publications

6.9K

Citations

346

Authors

159

Institutions

Publications per year

2017–2026

25

Authors

346

  1. TH
    Takahiro Hara

    The University of Osaka, Japan

    9 publications · 176 citations

    9

    h-index

  2. SN
    Shojiro Nishio

    The University of Osaka, Japan

    8 publications · 159 citations

    8

    h-index

  3. JX
    Jeffrey Xu Yu

    Chinese University of Hong Kong, Hong Kong SAR China

    7 publications · 624 citations

    7

    h-index

  4. XL
    Xuemin Lin

    UNSW Sydney, Australia

    6 publications · 566 citations

    6

    h-index

  5. GX
    Guoqing Xiao

    Hunan University, China

    5 publications · 186 citations

    5

    h-index

1–5 of 346

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 15 publications · 487 citations

    13

    h-index

  2. Proceedings of the VLDB Endowment

    PVLDB · 8 publications · 856 citations

    8

    h-index

  3. ACM Transactions on Database Systems

    TODS · 6 publications · 483 citations

    6

    h-index

  4. The VLDB Journal

    4 publications · 125 citations

    4

    h-index

  5. Distributed and Parallel Databases

    4 publications · 150 citations

    4

    h-index

1–5