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The Scientific World Journal
Volume 2013 (2013), Article ID 915963, 14 pages
Research Article

A Relation Routing Scheme for Distributed Semantic Media Query

1Key Laboratory of Knowledge Processing and Networked Manufacturing, University of Hunan Province, Xiangtan 411201, China
2School of Computer Science and Technology, Nanjing Normal University, Nanjing 210023, China
3Hunan University of Science and Technology, Xiangtan 411201, China
4Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

Received 11 August 2013; Accepted 4 September 2013

Academic Editors: K. Dejhan, J. He, and C. L. Hsu

Copyright © 2013 Zhuhua Liao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Performing complex semantic queries over large-scale distributed media contents is a challenging task for rich media applications. The dynamics and openness of data sources make it uneasy to realize a query scheme that simultaneously achieves precision, scalability, and reliability. In this paper, a novel relation routing scheme (RRS) is proposed by renovating the routing model of Content Centric Network (CCN) for directly querying large-scale semantic media content. By using proper query model and routing mechanism, semantic queries with complex relation constrains from users can be guided towards potential media sources through semantic guider nodes. The scattered and fragmented query results can be integrated on their way back for semantic needs or to avoid duplication. Several new techniques, such as semantic-based naming, incomplete response avoidance, timeout checking, and semantic integration, are developed in this paper to improve the accuracy, efficiency, and practicality of the proposed approach. Both analytical and experimental results show that the proposed scheme is a promising and effective solution for complex semantic queries and integration over large-scale networks.