By Topic

Top-k closest pairs join query: an approximate algorithm for large high dimensional data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Angiulli, F. ; ICAR-CNR, Universita della Calabria, Rende, Italy ; Pizzuti, C.

In This work we present a novel approximate algorithm to calculate the top k closest pairs join query of two large and high dimensional data sets. The algorithm has worst case time complexity O(d2nk) and space complexity O(nd) and guarantees a solution within a O(d1+ 12 /) factor of the exact one, where t ∈ {1,2,..., ∞} denotes the Minkowski metrics Lt of interest and d the dimensionality. It makes use of the concept of space filling curve to establish an order between the points of the space and performs at most d + 1 sorts and scans of the two data sets. During a scan, each point from one data set is compared with its closest points, according to the space filling curve order, in the other data set and points whose contribution to the solution has already been analyzed are detected and eliminated. Experimental results on real and synthetic data sets show that our algorithm (i) behaves as an exact algorithm in low dimensional spaces; (ii) it is able to prune the entire (or a considerable fraction of the) data set even for high dimensions if certain separation conditions are satisfied; (iii) in any case it returns a solution within a small error to the exact one.

Published in:

Database Engineering and Applications Symposium, 2004. IDEAS '04. Proceedings. International

Date of Conference:

7-9 July 2004