By Topic

Efficient Query Processing in Arbitrary Subspaces Using Vector Approximations

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

4 Author(s)
Kriegel, H.-P. ; Inst. for Informatics, Univ. of Munich ; Kroger, P. ; Schubert, M. ; Zhu, Z.

In this paper, we introduce the partial vector approximation file, an extension of the well known vector approximation file that is constructed to efficiently answer partial similarity queries in any possible subspace which is not known beforehand. The idea of the partial VA-file is to divide the VA-file into a separate file for each dimension and only load the dimensions that are necessary to answer the query. Thus, the partial VA-file is constructed to improve the query performance for systems that have to cope with a wide variety of previously unknown query subspaces. We propose novel algorithms for partial kNN and epsiv-range queries based on the new partial VA-file. In our experiments, we demonstrate that our proposed partial VA-file with the novel algorithms improves the average query performance in comparison to the original VA-file when answering partial similarity queries

Published in:

Scientific and Statistical Database Management, 2006. 18th International Conference on

Date of Conference:

0-0 0