External Memory Kd-trees

External Memory kd-Trees is a spatial access method that extends the classical k-dimensional tree to external memory models by optimizing block-aligned node layouts and I/O complexity for efficient range and nearest-neighbor queries on large-scale datasets stored on secondary storage.

12

Publications

1.9K

Citations

50

Authors

7

Institutions

Publications per year

2017–2026

2

Authors

50

  1. XS
    Xiaojia Song

    San Diego State University, United States

    1 publications · 18 citations

    1

    h-index

  2. RT
    Richard T. Snodgrass

    University of Arizona, United States

    1 publications · 845 citations

    1

    h-index

  3. MK
    Martin Kersten

    Centrum Wiskunde & Informatica, Netherlands

    1 publications · 447 citations

    1

    h-index

  4. PC
    Peter C. Lockemann

    Karlsruhe Institute of Technology, Germany

    1 publications · 58 citations

    1

    h-index

  5. DK
    Donald Kossmann

    ETH Zurich, Switzerland

    1 publications · 79 citations

    1

    h-index

1–5 of 50

Venues

  1. arXiv (Cornell University)

    Repository · 1 publications · 10 citations

    1

    h-index

  2. 1

    h-index

  3. Medical Entomology and Zoology

    1 publications · 447 citations

    1

    h-index

  4. Future Generation Computer Systems

    FGCS · 1 publications · 18 citations

    1

    h-index

1–4