Data Management in Grid and Peer-to-Peer Systems (e-bog) af -

Data Management in Grid and Peer-to-Peer Systems e-bog

436,85 DKK (inkl. moms 546,06 DKK)
First International Conference on Data Management in Grid and Peer-to-Peer (P2P) Systems, Globe 2008 Data management can be achieved by different types of systems: from centralized file management systems to grid and P2P systems passing through distributed systems, par- lel systems, and data integration systems. An increase in the demand of data sharing from different sources accessible throug...
E-bog 436,85 DKK
Forfattere Hameurlain, Abdelkader (redaktør)
Forlag Springer
Udgivet 25 august 2008
Genrer UKN
Sprog English
Format pdf
Beskyttelse LCP
ISBN 9783540851769
First International Conference on Data Management in Grid and Peer-to-Peer (P2P) Systems, Globe 2008 Data management can be achieved by different types of systems: from centralized file management systems to grid and P2P systems passing through distributed systems, par- lel systems, and data integration systems. An increase in the demand of data sharing from different sources accessible through networks has led to proposals for virtual data in- gration approach. The aim of data integration systems, based on the mediator-wrapper architecture, is to provide uniform access to multiple distributed, autonomous and h- erogeneous data sources. Heterogeneity may occur at various levels (e. g. , different ha- ware platforms, operating systems, DBMS). For more than ten years, research topics such as grid and P2P systems have been very active and their synergy has been pointed out. They are important for scale d- tributed systems and applications that require effective management of voluminous, distributed, and heterogeneous data. This importance comes out of characteristics offered by these systems (e. g. , autonomy and the dynamicity of nodes, decentralized control for scaling). Today, the grid and P2P systems intended initially for intensive computing and file sharing are open to the management of voluminous, heteroge- ous, and distributed data in a large-scale environment.