By Topic

An Adaptive PPM Prediction Model Based on Pruning Technique

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
$33 $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

5 Author(s)

The key issue of Web prefetching is to establish an effective user prediction model. Prediction by partial match (PPM) is one of the context models used in the Web prefetching area. The high space complexity and low efficiency of the PPM model affect its application. In this paper, we make use of pruning technique and propose a new adaptive PPM model based on Zipf's law and Web access characteristics. The experiments have shown that this model not only can be used to make predictions dynamically, but also has relative lower space complexity and higher prediction accuracy.

Published in:

Semantics, Knowledge and Grid, 2005. SKG '05. First International Conference on

Date of Conference:

27-29 Nov. 2005