Abstract
Conventional multimedia information retrieval systems use a central system to store and index multimedia data. Inherent limitations of such a central approach surface many problems, such as insufficient bandwidth, server overloading and failures. In order to retain the original system and control the cost, the paper shares the access pressure of central servers by constructing a peer-to-peer network based on interests. A user’s interest is computed by mining this user’s search behaviour periodically. Then researcher form a peer-to-peer network based on interests by clustering peers with similar interests. Centralized server will push relevant multimedia information to certain communities on time. By this way, uses can further clear what they want, and useless retrievals on servers will be dramatically decreased. The experimental results evaluate the average search path length of unstructured P2P network, semi-distribution P2P network and the P2P network based on interest community and demonstrate the efficiency of the approach.
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Acknowledgments
This paper is supported by the National Science and Technology Support Project (2012BAH02F02-01) and the National High Technology Research and Development Program of China (2011AA01A107).
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Feng, S., Liu, S., Wang, Y. (2013). An Approach for Large Scale Retrieval Using Peer-to-Peer Network Based on Interest Community. In: Wong, W.E., Ma, T. (eds) Emerging Technologies for Information Systems, Computing, and Management. Lecture Notes in Electrical Engineering, vol 236. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7010-6_8
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DOI: https://doi.org/10.1007/978-1-4614-7010-6_8
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