Multidimensional reverse k NN search

Multidimensional reverse k NN search Given a multidimensional point q , a reverse k nearest neighbor (R k NN) query retrieves all the data points that have q as one of their k nearest neighbors. Existing methods for processing such queries have at least one of the following deficiencies: they (i) do not support arbitrary values of k , (ii) cannot deal efficiently with database updates, (iii) are applicable only to 2D data but not to higher dimensionality, and (iv) retrieve only approximate results. Motivated by these shortcomings, we develop algorithms for exact R k NN processing with arbitrary values of k on dynamic, multidimensional datasets. Our methods utilize a conventional data-partitioning index on the dataset and do not require any pre-computation. As a second step, we extend the proposed techniques to continuous R k NN search, which returns the R k NN results for every point on a line segment. We evaluate the effectiveness of our algorithms with extensive experiments using both real and synthetic datasets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Multidimensional reverse k NN search

Loading next page...
 
/lp/springer_journal/multidimensional-reverse-k-nn-search-Ci3GSYX1yM
Publisher
Springer-Verlag
Copyright
Copyright © 2007 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-005-0168-2
Publisher site
See Article on Publisher Site

Abstract

Given a multidimensional point q , a reverse k nearest neighbor (R k NN) query retrieves all the data points that have q as one of their k nearest neighbors. Existing methods for processing such queries have at least one of the following deficiencies: they (i) do not support arbitrary values of k , (ii) cannot deal efficiently with database updates, (iii) are applicable only to 2D data but not to higher dimensionality, and (iv) retrieve only approximate results. Motivated by these shortcomings, we develop algorithms for exact R k NN processing with arbitrary values of k on dynamic, multidimensional datasets. Our methods utilize a conventional data-partitioning index on the dataset and do not require any pre-computation. As a second step, we extend the proposed techniques to continuous R k NN search, which returns the R k NN results for every point on a line segment. We evaluate the effectiveness of our algorithms with extensive experiments using both real and synthetic datasets.

Journal

The VLDB JournalSpringer Journals

Published: Jul 1, 2007

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off