A Framework for Prefetching Relevant Web Pages using Predictive Prefetching Engine (PPE)

TL;DR

This paper proposes a framework utilizing a Predictive Prefetching Engine that employs data mining on search logs to improve web page relevancy by prefetching likely next pages based on user navigation patterns.

Contribution

Introduces a novel framework with a Predictive Prefetching Engine that uses data mining rules to enhance web page prefetching based on user behavior.

Findings

Improves web page relevancy through predictive prefetching.

Uses data mining algorithms on search logs to identify user navigation patterns.

Employs agents to execute prefetching based on derived rules.

Abstract

This paper presents a framework for increasing the relevancy of the web pages retrieved by the search engine. The approach introduces a Predictive Prefetching Engine (PPE) which makes use of various data mining algorithms on the log maintained by the search engine. The underlying premise of the approach is that in the case of cluster accesses, the next pages requested by users of the Web server are typically based on the current and previous pages requested. Based on same, rules are drawn which then lead the path for prefetching the desired pages. To carry out the desired task of prefetching the more relevant pages, agents have been introduced.