Exploiting semantic proximities for content search over p2p networks

Doulamis, ND; Karamolegkos, PN; Doulamis, A; Nikolakopoulos, I

dc.contributor.author	Doulamis, ND	en
dc.contributor.author	Karamolegkos, PN	en
dc.contributor.author	Doulamis, A	en
dc.contributor.author	Nikolakopoulos, I	en
dc.date.accessioned	2014-03-01T01:30:29Z
dc.date.available	2014-03-01T01:30:29Z
dc.date.issued	2009	en
dc.identifier.issn	0140-3664	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19598
dc.subject	Clustering	en
dc.subject	Content search	en
dc.subject	Peer-to-peer	en
dc.subject.classification	Computer Science, Information Systems	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.classification	Telecommunications	en
dc.subject.other	Client server computer systems	en
dc.subject.other	Semantics	en
dc.subject.other	Average numbers	en
dc.subject.other	Clustering	en
dc.subject.other	Content search	en
dc.subject.other	Dynamic algorithms	en
dc.subject.other	Eigen decompositions	en
dc.subject.other	matrixes	en
dc.subject.other	P2p networks	en
dc.subject.other	Peer networks	en
dc.subject.other	Peer nodes	en
dc.subject.other	Peer-to-peer	en
dc.subject.other	Peer-to-peer applications	en
dc.subject.other	Practical issues	en
dc.subject.other	Search techniques	en
dc.subject.other	Semantic partitioning	en
dc.subject.other	Spectral clustering	en
dc.subject.other	Information theory	en
dc.title	Exploiting semantic proximities for content search over p2p networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.comcom.2008.12.005	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.comcom.2008.12.005	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	In this paper, we address the issue, Of Content search over peer-to peer networks. We use the Concept Of semantic proximity that exploits the commonalities Of interest exhibited among peer user's so as to decompose the net,work into semantic clusters. We initially define search entropy, as a metric indicating the average number of packets required to locate the requested content. Then, spectral clustering is used to organize the peer nodes into semantic clusters so that (a) the probability that a node locates content within its own cluster is maximized, While simultaneously: (b) the respective probability of finding this Content outside this cluster is minimized. The proposed semantic partitioning algorithm is then extended Into a hierarchical two-tier scheme, in which practical issues arising for the deployment of or peer-to-peer (p2p) application call be more easily addressed. After, the system has been initialized, a dynamic algorithm places new users that the p2p network into appropriately selected clusters and also handles peer departures without the need for matrix eigen decomposition process which is necessary for the assessment of the initial static partitioning,. Our experimental results Validate that (a) our static partitioning outperforms traditional and novel search techniques and (b) our dynamic algorithm is able to efficiently track the system's progression maintaining file search entropy close to the initially assessed levels. (C) 2008 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Computer Communications	en
dc.identifier.doi	10.1016/j.comcom.2008.12.005	en
dc.identifier.isi	ISI:000264910700005	en
dc.identifier.volume	32	en
dc.identifier.issue	5	en
dc.identifier.spage	814	en
dc.identifier.epage	827	en