Content Based Clustering for Semantic P2P Data Integration
Ahmed Moujane, Dalila Chiadmi, Laila Benhlima, FaouziaWadjinny
Journal of Networking Technology, Vol. 3, No. 1, 2012.
DOI: 
Link: http://www.dline.info/jnt/fulltext/v3n1/2.pdf
Abstract:
Clustering peers based on the semantics of their content is one of the most difficult and important taskin P2P data integration systems because it enhances data search and integration significantly. Currently super-peer networks, such as the Edutella network, do not provide sophisticated means for such a â€œsemantic clusteringâ€ of peers.In fact, most solutions try only to combine the advantages of data integration and P2P technologies to overcome centralized solutions shortcomings without taking into account content based semantic and efficiency to deal with network dynamicity. In this paper, we investigate P2P computing and data integration fundamentals and detail the challenges that face the P2P data integration process. In addition, we presentour approach for semantic clustering in a super peer architecture based on a vector space model forour P2P semantic data integration framework. In a first stage, we detail the various modules of our framework and specify the functions of each one. Then, we detailed the different levels of knowledge we take into consideration for the semantic clustering that we adopted using ontology alignment techniques. Finally, we explain how we manage network dynamicity and how semantic should be adjusted accordingly.