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Title Large-scale data analytics / Aris Gkoulalas-Divanis, Abderrahim Labbi, editors
Published New York : Springer, [2014]
©2014
Table of Contents
1.The Family of Map-Reduce / Anna Liu1
2.Optimization of Massively Parallel Data Flows / Volker Markl41
3.Mining Tera-Scale Graphs with "Pegasus": Algorithms and Discoveries / Christos Faloutsos75
4.Customer Analyst for the Telecom Industry / Michal Shmueli-Scheuer101
5.Machine Learning Algorithm Acceleration Using Hybrid (CPU-MPP) MapReduce Clusters / John R. Williams129
6.Large-Scale Social Network Analysis / Carmine Paolino155
7.Visual Analysis and Knowledge Discovery for Text / Michael Granitzer189
8.Practical Distributed Privacy-Preserving Data Analysis at Large Scale / John Canny219
 Index253
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Description 1 online resource (xxiii, 257 pages) : illustrations
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
Summary 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
Bibliography Includes bibliographical references and index
Notes Print version record
Subject Data mining.
Form Electronic book
Author Gkoulalas-Divanis, Aris, editor
Labbi, Abderrahim, editor
LC no. 2013954541
ISBN 9781461492429 (electronic bk.)
1461492424 (electronic bk.)