G-ToPSS: Fast Filtering of Graph-based Metadata

Milenko Petrovic, Haifeng Liu, and Hans-Arno Jacobsen.

In World Wide Web Conference, pages 539-547, Chiba, Japan, May 2005.
Nominated for best paper award, i.e., one of four finalist papers. Acceptance rate: 14%. Number of submissions: 550.

Abstract

RDF is increasingly being used to represent metadata. RDF Site Summary (RSS) is an application of RDF on the Web that has considerably grown in popularity. However, the way RSS systems operate today does not scale well. In this paper we introduce G-ToPSS, a scalable publish/subscribe system for selective information dissemination. G-ToPSS is particularly well suited for applications that deal with largevolume content distribution from diverse sources. RSS is an instance of the content distribution problem. G-ToPSS allows use of ontology as a way to provide additional information about the data. Furthermore, in this paper we show how G-ToPSS can support RDFS class taxonomies. We have implemented and experimentally evaluated G-ToPSS and we provide results in the paper demonstrating its scalability compared to alternatives.

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Tags: algorithms, content-based publish/subscribe, event processing, publish/subscribe, topss, graph-based pub/sub


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