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Titlebook: Large-Scale Data Analytics; Aris Gkoulalas-Divanis,Abderrahim‘Labbi Book 2014 Springer Science+Business Media New York 2014 Big data.GPU p

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发表于 2025-3-21 19:45:55 | 显示全部楼层 |阅读模式
书目名称Large-Scale Data Analytics
编辑Aris Gkoulalas-Divanis,Abderrahim‘Labbi
视频video
概述Provides cutting-edge research in large-scale data analytics from diverse scientific areas.Surveys varied subject areas and reports on individual results of research in the field.Shares many tips and
图书封面Titlebook: Large-Scale Data Analytics;  Aris Gkoulalas-Divanis,Abderrahim‘Labbi Book 2014 Springer Science+Business Media New York 2014 Big data.GPU p
描述.This edited book collects state-of-the-art research related to large-scale data analytics that has been accomplished over the last few years. This is among the first books devoted to this important area based on contributions from diverse scientific areas such as databases, data mining, supercomputing, hardware architecture, data visualization, statistics, and privacy..There is increasing need for new approaches and technologies that can analyze and synthesize very large amounts of data, in the order of petabytes, that are generated by massively distributed data sources. This requires new distributed architectures for data analysis. Additionally, the heterogeneity of such sources imposes significant challenges for the efficient analysis of the data under numerous constraints, including consistent data integration, data homogenization and scaling, privacy and security preservation. The authors also broaden reader understanding of emerging real-world applications in domains such as customer behavior modeling, graph mining, telecommunications, cyber-security, and social network analysis, all of which impose extra requirements for large-scale data analysis..Large-Scale Data Analytics.
出版日期Book 2014
关键词Big data; GPU programming; data mining; graph mining; hardware acceleration; high performance computing; l
版次1
doihttps://doi.org/10.1007/978-1-4614-9242-9
isbn_softcover978-1-4939-4225-1
isbn_ebook978-1-4614-9242-9
copyrightSpringer Science+Business Media New York 2014
The information of publication is updating

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发表于 2025-3-21 23:54:51 | 显示全部楼层
Large-Scale Social Network Analysis,thms and libraries. In this chapter we provide an overview of the state of the art in the field of large scale social network analysis; in particular, we focus on parallel algorithms and libraries for the computation of network centrality metrics.
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The Family of Map-Reduce, a comprehensive survey for a family of approaches and mechanisms of large scale data analysis that have been implemented based on the original father idea of the MapReduce framework, and are currently gaining a lot of momentum in both research and industrial communities. Some case studies are discussed as well.
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