Update Propagation Strategies for Improving the Quality of Data on the Web
DocUID: 2001-004 Full Text:
PDF Author: Alexandros Labrinidis, Nick Roussopoulos
Abstract: Dynamically generated web pages are ubiquitous today but their high demand for resources creates a huge scalability problem at the servers. Traditional web caching is not able to solve this problem since it cannot provide any guarantees as to the freshness of the cached data. A robust solution to the problem is web materialization, where pages are cached at the web server and constantly updated in the background, resulting in fresh data accesses on cache hits. In this work, we define Quality of Data metrics to evaluate how fresh the data served to the users is. We then focus on the update scheduling problem: given a set of views that are materialized, find the best order to refresh them, in the presence of continuous updates, so that the overall Quality of Data (QoD) is maximized. We present a QoD-aware Update Scheduling algorithm that is adaptive and tolerant to surges in the incoming update stream. We performed extensive experiments using real traces and synthetic ones, which show that our algorithm consistently outperforms FIFO scheduling by up to two orders of magnitude.
Published In: Proc. of the 27th International Conference on Very Large Data Bases
ISBN: 1-55860-804-4
Pages: pp. 391-400
Place Published: Rome, Italy
Year Published: 2001
Project: Others Subject Area: Web Databases
Publication Type: Conference Paper
Sponsor: Others