G-ToPSS: Fast Filtering of Graph-based Metadata

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

Special issue on WWW’05 in Semantic Web Journal, 3(4)294-310, December 2005.
Elsevier Science Publisher.

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

Download


Readers who enjoyed the above work, may also like the following:


  • Optimized Cluster-based Filtering Algorithm for Graph Metadata.
    Haifeng Liu, Milenko Petrovic, Hans-Arno Jacobsen, and Zhaohui Wu.
    Information Sciences, 181(24)5468-5484, December 2011.
    Tags: graph-based pub/sub
  • Efficient and Scalable Filtering of Graph-based Metadata.
    Haifeng Liu, Milenko Petrovic, and Hans-Arno Jacobsen.
    J. Web Sem., 3(4)294-310, 2005.
    Tags: graph-based pub/sub
  • 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.
    Tags: algorithms, content-based publish/subscribe, event processing, publish/subscribe, topss, graph-based pub/sub