By Topic

MAMCost: Global and Local Estimates leading to Robust Cost Estimation of Similarity Queries

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

3 Author(s)
Baioco, G.B. ; Univ. of Sao Paulo at S. Carlos, Sao Carlos ; Traina, A.J.M. ; Traina, C.

This paper presents an effective cost model to estimate the number of disk accesses (I/O cost) and the number of distance calculations (CPU cost) to process similarity queries over data indexed by metric access methods. Two types of similarity queries were taken into consideration: range and k-nearest neighbor queries. The main point of the cost model is considering not only global parameters of the data set but also the local data distribution. The model takes advantage of the intrinsic dimension of the data set, estimated by its correlation fractal dimension. Experiments were performed on real and synthetic data sets, with different sizes and dimensions, in order to validate the proposed model. They confirmed that the estimations are accurate, within the range achieved by real queries.

Published in:

Scientific and Statistical Database Management, 2007. SSBDM '07. 19th International Conference on

Date of Conference:

9-11 July 2007