Indexing the Distance: An Efficient Method to KNN Processing
Summary: iDistance transforms high-dimensional KNN search into 1D range queries by partitioning data around reference points and indexing distance keys with a B+-tree. Flexible partition/reference selection adapts the index to data distribution, outperforming fixed metric indexes. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Cui Yu (National University of Singapore)
- 2. Beng Chin Ooi (National University of Singapore)
- 3. Kian-Lee Tan (National University of Singapore)
- 4. H. V. Jagadish (University of Michigan)
BibTeX Citation
@article{yu_vldb01,
title = {{Indexing the Distance: An Efficient Method to KNN Processing}},
author = {Yu, Cui and Ooi, Beng Chin and Tan, Kian-Lee and Jagadish, H. V.},
journal = {PVLDB},
series = {{VLDB} '01},
year = {2001}
}
Incoming Citations (Sorted by Pagerank)
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