Data Mangement in Cloud, Grid and P2P Systems, 2012
5th International Conference, Globe 2012, Vienna, Austria, September 5-6, 2012, Proceedings
Information Systems and Applications, incl. Internet/Web, and HCI Series

Coordinators: Hameurlain Abdelkader, Hussain Farookh Khadeer, Morvan Franck, Tjoa A Min

Language: English
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113 p. · 15.5x23.5 cm · Paperback
This book constitutes the refereed proceedings of the 5th International Conference on Data Management in Grid and Peer-to-Peer Systems, Globe 2012, held in Vienna, Austria, in September 2012 in conjunction with DEXA 2012.
The 9 revised full papers presented were carefully reviewed and selected from 15 submissions. The papers are organized in topical sections on data management in the cloud, cloud MapReduce and performance evaluation, and data stream systems and distributed data mining.

TransElas: Elastic Transaction Monitoring for Web2.0 Applications.- Recoverable Encryption through Noised Secret over a Large Cloud.- A Bigtable/MapReduce-Based Cloud Infrastructure for Effectively and Efficiently Managing Large-Scale Sensor Networks.- MapReduce Applications in the Cloud: A Cost Evaluation of Computation and Storage.- Scalability Issues in Designing and Implementing Semantic Provenance Management Systems.- Performance Characteristics of Virtualized Platforms from Applications Perspective.- Open Execution Engines of Stream Analysis Operations.- Improving Content Delivery by Exploiting the Utility of CDN Servers.-  Using MINING@HOME for Distributed Ensemble Learning.

Recoverable Encryption through Noised Secret over a Large Cloud.- A Bigtable/MapReduce-Based Cloud Infrastructure for Effectively and Efficiently Managing Large-Scale Sensor Networks.- MapReduce Applications in the Cloud: A Cost Evaluation of Computation and Storage.- Scalability Issues in Designing and Implementing Semantic Provenance Management Systems.- Performance Characteristics of Virtualized Platforms from Applications Perspective.- Open Execution Engines of Stream Analysis Operations.- Improving Content Delivery by Exploiting the Utility of CDN Servers.-  Using MINING@HOME for Distributed Ensemble Learning.

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