GrouPeer: A system for clustering PDMSs

Kantere, V; Bousounis, D; Sellis, T

dc.contributor.author	Kantere, V	en
dc.contributor.author	Bousounis, D	en
dc.contributor.author	Sellis, T	en
dc.date.accessioned	2014-03-01T02:02:19Z
dc.date.available	2014-03-01T02:02:19Z
dc.date.issued	2011	en
dc.identifier.issn	21508097	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/29301
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-84863746342&partnerID=40&md5=59011e5a60a66f851610cc3577816e56	en
dc.subject.other	Approximate query	en
dc.subject.other	Clustering mechanism	en
dc.subject.other	Dynamic clustering	en
dc.subject.other	High quality	en
dc.subject.other	Iterative learning process	en
dc.subject.other	Local information	en
dc.subject.other	Propagation paths	en
dc.subject.other	Query similarity	en
dc.subject.other	Schema matching	en
dc.subject.other	Semantic clustering	en
dc.subject.other	Structured data	en
dc.subject.other	Degradation	en
dc.subject.other	Query processing	en
dc.subject.other	Semantics	en
dc.subject.other	Search engines	en
dc.title	GrouPeer: A system for clustering PDMSs	en
heal.type	journalArticle	en
heal.publicationDate	2011	en
heal.abstract	Sharing structured data in a PDMS is hard due to schema heterogeneity and peer autonomy. To overcome heterogeneity, peer databases employ mappings that partially match local information to that of their direct neighbors. Traditionally, a query is successively rewritten along the propagation path on each peer. This results in gradual query degradation and the inability to retrieve data pertinent to the original version, even from peers that store such data. This demonstration presents GrouPeer, a system that overcomes the query degradation problem and enables the dynamic clustering of the overlay according to the semantics of the peer data, utilizing normal query traffic. Peers are provided with a methodology that allows them to choose which rewritten version of a query to answer and discover remote information-rich sources. The demonstration illustrates the functionalities in the clustering mechanism of GrouPeer: approximate query rewriting, query similarity methodology, construction of new mappings, iterative learning process, employment of automatic schema matching, and proves the capability of the system to perform gradual semantic clustering and enable high quality answers to peer queries. © 2011 VLDB Endowment.	en
heal.journalName	Proceedings of the VLDB Endowment	en
dc.identifier.volume	4	en
dc.identifier.issue	12	en
dc.identifier.spage	1371	en
dc.identifier.epage	1374	en