Efficient and scalable filtering of graph-based metadata

Abstract

RDF Site Summaries constitute an application of RDF on the Web that has considerably grown in popularity. However, the way RSS systems operate today limits their scalability. Current RSS feed arregators follow a pull-based architecture model, which is not going to scale with the increasing number of RSS feeds becoming available on the Web. In this paper, we introduce G-ToPSS, a scalable publish/subscribe system for selective information dissemination. G-ToPSS only sends newly updated information to the interested user and follows a push-based architecture model. G-ToPSS is particularly well suited for applications that deal with large-volume content distribution from diverse sources. G-ToPSS allows use of an ontology as a way to provide additional information about the data disseminated. We have implemented and experimentally evaluated G-ToPSS and we provide results demonstrating its scalability compared to alternative approaches. In addition, we describe an application of G-ToPSS and RSS to a Web-based content management system that provides an expressive, efficient, and convenient update notification dissemination system.