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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