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
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50
Authors
7
Institutions
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2017–2026
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Proceedings of the 27th International Conference on Very Large Data Bases
Peter M. G. Apers, Paolo Atzeni, Stefano Ceri et al. · 2001 · 845 citations
Proceedings of the 25th International Conference on Very Large Data Bases
Malcolm P. Atkinson, Maria E. Orłowska, Patrick Valduriez et al. · 1999 · 221 citations
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San Diego State University
San Diego, United States
1 h-index
2 publications
36 citations
Samsung (United States)
Ridgefield Park, United States
1 h-index
1 publications
18 citations
Korea Advanced Institute of Science and Technology
Daejeon, South Korea
1 h-index
1 publications
18 citations
General Motors (Poland)
Gliwice, Poland
1 h-index
1 publications
68 citations
Max Planck Institute for Informatics
Saarbrücken, Germany
1 h-index
5 publications
175 citations
University of Michigan
Ann Arbor, United States
1 h-index
2 publications
136 citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
1 h-index
1 publications
18 citations
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Repository · 1 publications · 10 citations
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Repository · 1 publications · 35 citations
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1 publications · 447 citations
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FGCS · 1 publications · 18 citations
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