Indexing the Distance: An Efficient Method to KNN Processing

By Cui Yu, Beng Chin Ooi, Kian-Lee Tan, Hosagrahar Visvesvaraya Jagadish. Very Large Data Bases Conference, 2001

319 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/dfbbf264d1d099f86d26ff58f80c80cfac00ca52

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Wei Lu
2. Cyrus Shahabi
3. Kiyoung Yang
4. Dengsheng Zhang
5. Ying Liu
6. Yanyan Shen
7. Guojun Lu
8. Beng Chin Ooi

Citations in the corpus

• A survey of content-based image retrieval with high-level semantics (2007, methodology): “To relieve this problem, high-dimensional indexing algorithms such as X-tree [135], VA-file [134], and i-Distance [136] have been introduced.”
• A PCA-based similarity measure for multivariate time series (2004, methodology): “Due to the high dimensionality of MTS items, the distance-based indexing techniques, such as iDistance [40], would perform better for MTS datasets.”
• iDistance: An adaptive B+-tree based indexing method for nearest neighbor search (2005, result): “This paper is an extended version of our earlier paper [Yu et al. 2001].”
• Efficient Processing of k Nearest Neighbor Joins using MapReduce (2012, methodology): “[9, 20] Given a partition P i , ∀o ∈ P i , the necessary condition that |q, o| ≤ θ is: max{L(P i ), |pi, q| − θ} ≤ |pi, o| ≤ min{U(P i ), |pi, q|+ θ} (4)”