Logo - springer
Slogan - springer

Computer Science - Database Management & Information Retrieval | Large-Scale Data Analytics

Large-Scale Data Analytics

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

2014, XXIII, 257 p. 83 illus.

Available Formats:
eBook
Information

Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.

You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.

After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.

 
$79.99

(net) price for USA

ISBN 978-1-4614-9242-9

digitally watermarked, no DRM

Included Format: PDF and EPUB

download immediately after purchase


learn more about Springer eBooks

add to marked items

Hardcover
Information

Hardcover version

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

 
$109.00

(net) price for USA

ISBN 978-1-4614-9241-2

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days


add to marked items

  • 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

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.

Content Level » Research

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

Related subjects » Business Information Systems - Database Management & Information Retrieval - Information Systems and Applications - Security and Cryptology

Table of contents 

The Family of Map-Reduce.- Optimization of Massively Parallel Data Flows.- Mining Tera-Scale Graphs with "Pegasus".- Customer Analyst for the Telecom Industry.- Machine Learning Algorithm Acceleration using Hybrid (CPU-MPP) MapReduce Clusters.- Large-Scale Social Network Analysis.- Visual Analysis and Knowledge Discovery for Text.- Practical Distributed Privacy-Preserving Data Analysis at Large Scale.

Popular Content within this publication 

 

Articles

Read this Book on Springerlink

Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Database Management.