Efficiently Detecting Webpage Updates Using Samples

Due to resource constraints, Web archiving systems and search engines usually have difficulties keeping the local repository completely synchronized with the Web. To address this problem, sampling-based techniques periodically poll a subset of webpages in the local repository to detect changes on the Web, and update the local copies accordingly. The goal of such an approach is to discover as many changed webpages as possible within the boundary of the available resources. In this paper we advance the state-of-art of the sampling-based techniques by answering a challenging question: Given a sampled webpage that has been updated, which other webpages are also likely to have changed? We propose a set of sampling policies with various downloading granularities, taking into account the link structure, the directory structure, and the content-based features. We also investigate the update history and the popularity of the webpages to adaptively model the download probability. We ran extensive experiments on a real web data set of about 300,000 distinct URLs distributed among 210 websites. The results showed that our sampling-based algorithm can detect about three times as many changed webpages as the baseline algorithm. It also showed that the changed webpages are most likely to be found in the same directory and the upper directories of the changed sample. By applying clustering algorithm on all the webpages, pages with similar change pattern are grouped together so that updated webpages can be found in the same cluster as the changed sample. Moreover, our adaptive downloading strategies significantly outperform the static ones in detecting changes for the popular webpages.