By Abdelkader Hameurlain, Tran Khanh Dang, Franck Morvan
This e-book constitutes the refereed lawsuits of the seventh foreign convention on information administration in Grid and Peer-to-Peer platforms, Globe 2014, held in Munich, Germany, in September 2014, along side DEXA 2014. The 7 revised complete papers provided have been conscientiously reviewed and chosen from 14 submissions. The papers are equipped within the following topical sections: question processing in cloud, grid, and P2P platforms: optimization and advice; MapReduce framework: info privateness and similarity seek; info administration in grid platforms: protocol and restoration failure.
Read or Download Data Management in Cloud, Grid and P2P Systems: 7th International Conference, Globe 2014, Munich, Germany, September 2-3, 2014. Proceedings PDF
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Additional resources for Data Management in Cloud, Grid and P2P Systems: 7th International Conference, Globe 2014, Munich, Germany, September 2-3, 2014. Proceedings
The multiplication of A by Ft generates a matrix R (IDA output). e. the ith row of the key matrix), 2) a body storing Ri . Mi is a chunk to send to a mapper. Once m chunks are collected by a host, the key matrix is obtained from the m headers. The product result of the inverted key matrix by the content of the chunks gives the original data. B. Cheikh, H. Abbes, and G. Fedak MapReduce The principle of MapReduce consists of splitting the data into parts so their process is simultaneously done. The model deﬁnes two main functions, the Map function processes input chunks and the Reduce function processes the output of Map tasks and outputs ﬁnal results.
If m n: we reduce communications but we weaken the security and considerably increase redundancy. • if m ≈ n: we provide better security, we maintain an acceptable level of redundancy but we increase communications between mappers. – Mappers allocation: Threats can occur at the mapper itself, as a malicious one, or during communications when intruders hearken the network. A malicious mapper can have access to data as it is charged to process it. This scenario is allowed in our system. Nevertheless, when a community of, at least, m malicious mappers cooperate to reveal their input, they may reveal all data, not only theirs.
E applications manipulating an enormous amount of data at large scale. Recently, many organizations have adopted the MapReduce model and have implemented their own frameworks such as Google MapReduce [DG04], Yahoo! Hadoop [Whi09] and BitDew [TMC+ 10]. Furthermore, this model has been adopted by many researchers in high performance computing, data intensive scientiﬁc analysis, large scale semantic annotation and machine learning. A large class of data processing systems using MapReduce is mainly running on local platform, such as clusters.
Data Management in Cloud, Grid and P2P Systems: 7th International Conference, Globe 2014, Munich, Germany, September 2-3, 2014. Proceedings by Abdelkader Hameurlain, Tran Khanh Dang, Franck Morvan