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Large-Scale Data Analytics

  • Aris Gkoulalas-Divanis
  • Abderrahim  Labbi

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Sherif Sakr, Anna Liu
    Pages 1-39
  3. Fabian Hueske, Volker Markl
    Pages 41-74
  4. David Konopnicki, Michal Shmueli-Scheuer
    Pages 101-127
  5. Sergio Herrero-Lopez, John R. Williams
    Pages 129-153
  6. Mattia Lambertini, Matteo Magnani, Moreno Marzolla, Danilo Montesi, Carmine Paolino
    Pages 155-187
  7. Christin Seifert, Vedran Sabol, Wolfgang Kienreich, Elisabeth Lex, Michael Granitzer
    Pages 189-218
  8. Back Matter
    Pages 253-257

About this book

Introduction

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.

Keywords

Big data GPU programming data mining graph mining hardware acceleration high performance computing large-scale analytics large-scale optimization large-scale visual analysis map-reduce privacy-preserving data analysis social network analysis

Editors and affiliations

  • Aris Gkoulalas-Divanis
    • 1
  • Abderrahim  Labbi
    • 2
  1. 1.IBM Research - IrelandMulhuddartIreland
  2. 2.IBM Research - ZurichRüschlikonSwitzerland

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-9242-9
  • Copyright Information Springer Science+Business Media New York 2014
  • Publisher Name Springer, New York, NY
  • eBook Packages Computer Science
  • Print ISBN 978-1-4614-9241-2
  • Online ISBN 978-1-4614-9242-9
  • Buy this book on publisher's site