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Ranked Reverse Nearest Neighbor Search
July 2008 (vol. 20 no. 7)
pp. 894-910
ASCII Text
x 
 
Ken C.K. Lee, Baihua Zheng, Wang-Chien Lee, "Ranked Reverse Nearest Neighbor Search," IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 7, pp. 894-910, July, 2008.
 
 
BibTex
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@article{ 10.1109/TKDE.2008.36,
author = {Ken C.K. Lee and Baihua Zheng and Wang-Chien Lee},
title = {Ranked Reverse Nearest Neighbor Search},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {20},
number = {7},
issn = {1041-4347},
year = {2008},
pages = {894-910},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2008.36},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Ranked Reverse Nearest Neighbor Search
IS - 7
SN - 1041-4347
SP894
EP910
EPD - 894-910
A1 - Ken C.K. Lee,
A1 - Baihua Zheng,
A1 - Wang-Chien Lee,
PY - 2008
KW - Database
KW - Query Processing
KW - Algorithms
KW - Nearest Neighbor
KW - Reverse Nearest Neighbor
VL - 20
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Given a set of data points P and a query point q in a multidimensional space, Reverse Nearest Neighbor (RNN) query finds data points in P whose nearest neighbors are q. Reverse k-Nearest Neighbor (RkNN) query (where k ≥ 1) generalizes RNN query to find data points whose kNNs include q. For RkNN query semantics, q is said to have influence to all those answer data points. The degree of q?s influence on a data point p (∈ P) is denoted by κp where q is the κp-th NN of p. We introduce a new variant of RNN query, namely, Ranked Reverse Nearest Neighbor (RRNN) query, that retrieves t data points most influenced by q, i.e., the t data points having the smallest κ?s with respect to q. To answer this RRNN query efficiently, we propose two novel algorithms, κ-Counting and κ-Browsing that are applicable to both monochromatic and bichromatic scenarios and are able to deliver results progressively. Through an extensive performance evaluation, we validate that the two proposed RRNN algorithms are superior to solutions derived from algorithms designed for RkNN query.

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Index Terms:
Database, Query Processing, Algorithms, Nearest Neighbor, Reverse Nearest Neighbor
Citation:
Ken C.K. Lee, Baihua Zheng, Wang-Chien Lee, "Ranked Reverse Nearest Neighbor Search," IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 7, pp. 894-910, July 2008, doi:10.1109/TKDE.2008.36
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