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Large-Scale Data Analytics

Szerző: Aris Gkoulalas-Divanis, Abderrahim Labbi

Nyelv:

Kötés: Puha kötésű

Kiadó: Springer-Verlag New York Inc.

Elérhetőség: Beszállítói készleten

Küldés 5-8 napon belül

19 223 Ft

This edited book collects state-of-the-art research related to large-scale data analytics that has b...

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Szerző

Aris Gkoulalas-Divanis, Abderrahim Labbi

Nyelv

Angol

Kötés

Könyv - Puha kötésű

Kiadva

2016

oldal

257

EAN

9781493942251

ISBN

1493942255

Enbook ID

13935067

Kiadó

Springer-Verlag New York Inc.

Súly

4336

Méretek

155 x 235 x 17

Teljes leírás

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 is organized in 8 chapters, each providing a survey of an important direction of large-scale data analytics or individual results of the emerging research in the field. The book presents key recent research that will help shape the future of large-scale data analytics, leading the way to the design of new approaches and technologies that can analyze and synthesize very large amounts of heterogeneous data. Students, researchers, professionals and practitioners will find this book an authoritative and comprehensive resource.

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