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  • © 2014

Large-Scale Data Analytics

  • 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

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xxiii
  2. The Family of Map-Reduce

    • Sherif Sakr, Anna Liu
    Pages 1-39
  3. Optimization of Massively Parallel Data Flows

    • Fabian Hueske, Volker Markl
    Pages 41-74
  4. Customer Analyst for the Telecom Industry

    • David Konopnicki, Michal Shmueli-Scheuer
    Pages 101-127
  5. Machine Learning Algorithm Acceleration Using Hybrid (CPU-MPP) MapReduce Clusters

    • Sergio Herrero-Lopez, John R. Williams
    Pages 129-153
  6. Large-Scale Social Network Analysis

    • Mattia Lambertini, Matteo Magnani, Moreno Marzolla, Danilo Montesi, Carmine Paolino
    Pages 155-187
  7. Visual Analysis and Knowledge Discovery for Text

    • Christin Seifert, Vedran Sabol, Wolfgang Kienreich, Elisabeth Lex, Michael Granitzer
    Pages 189-218
  8. Back Matter

    Pages 253-257

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.

Editors and Affiliations

  • IBM Research - Ireland, Mulhuddart, Ireland

    Aris Gkoulalas-Divanis

  • IBM Research - Zurich, Rüschlikon, Switzerland

    Abderrahim Labbi

Bibliographic Information

Buy it now

Buying options

eBook USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access