Large-Scale Data Analytics

Editors: Gkoulalas-Divanis, Aris, Labbi, Abderrahim (Eds.)

Free Preview
  • 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 insights into large-scale data analytics from authors and editors with long-term experience and specialization in the field
see more benefits

Buy this book

eBook n/a
  • ISBN 978-1-4614-9242-9
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
Hardcover n/a
  • ISBN 978-1-4614-9241-2
  • Free shipping for individuals worldwide
Softcover n/a
  • ISBN 978-1-4939-4225-1
  • Free shipping for individuals worldwide
About this book

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.

Table of contents (8 chapters)

Table of contents (8 chapters)
  • The Family of Map-Reduce

    Pages 1-39

    Sakr, Sherif (et al.)

  • Optimization of Massively Parallel Data Flows

    Pages 41-74

    Hueske, Fabian (et al.)

  • Mining Tera-Scale Graphs with “Pegasus”: Algorithms and Discoveries

    Pages 75-99

    Kang, U (et al.)

  • Customer Analyst for the Telecom Industry

    Pages 101-127

    Konopnicki, David (et al.)

  • Machine Learning Algorithm Acceleration Using Hybrid (CPU-MPP) MapReduce Clusters

    Pages 129-153

    Herrero-Lopez, Sergio (et al.)

Buy this book

eBook n/a
  • ISBN 978-1-4614-9242-9
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
Hardcover n/a
  • ISBN 978-1-4614-9241-2
  • Free shipping for individuals worldwide
Softcover n/a
  • ISBN 978-1-4939-4225-1
  • Free shipping for individuals worldwide
Loading...

Recommended for you

Loading...

Bibliographic Information

Bibliographic Information
Book Title
Large-Scale Data Analytics
Editors
  • Aris Gkoulalas-Divanis
  • Abderrahim Labbi
Copyright
2014
Publisher
Springer-Verlag New York
Copyright Holder
Springer Science+Business Media New York
eBook ISBN
978-1-4614-9242-9
DOI
10.1007/978-1-4614-9242-9
Hardcover ISBN
978-1-4614-9241-2
Softcover ISBN
978-1-4939-4225-1
Edition Number
1
Number of Pages
XXIII, 257
Number of Illustrations
83 b/w illustrations
Topics