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Big data technologies and applications
Furht B., Villanustre F., Springer International Publishing, New York, NY, 2016. 400 pp. Type: Book (978-3-319445-48-9)
Date Reviewed: Jun 1 2017

Big data technologies and applications is written by authors from academia and industry. The title of the book accurately represents the main goal of the text, namely to describe correctly the state of the art in big data technologies. The book is divided in three large sections.

The first section, which covers chapters 1 to 5, serves as a broad introduction to big data technologies. Adding upon the first chapter, which gives concise but precise enough definitions of big data, big data workflow, and big data architecture, the following four chapters discuss the main aspects of big data: data analytics, data learning techniques, data visualization, and deep learning. As such, each of these chapters can be used as a starting point for the graduate or advanced undergraduate student who wants to specialize in one of the previously mentioned areas. In this sense, it is very valuable that almost all chapters from Part 1 include a “practical” subsection, that is, one that discusses current popular application in the big data subdomain (analytics, visualization, and so on). Another plus is the extensive references given after each chapter, which may be of significant help for a student or researcher.

The second part of the book (chapters 6 to 11) discusses in detail Lexis/Nexis integrated solutions for big data. These chapters are very practical: chapter 6, for example, discusses the Lexis/Nexis High Performance Cluster Computing (HPCC) architecture both as hardware and as software support. The gem in this chapter is a good intro to the ECL programming language. The presentation of the language is simple, and the language itself is implicitly parallel, declarative, and allows one to easily define data processing and data flows. As it was shown, the ECL borrows its syntax from common languages such as Java and C++, which is a plus for any researcher already familiar with C++ or Java. Along with the valuable ECL description, the second part of the book discusses tools specifically developed by Lexis/Nexis for big data. In chapter 7, for instance, the reader can find a comprehensive description of SALT, a tool for automated linking of big data. The following two chapters cover the problems with analysis and models for big data, and chapter 10 provides a bird’s-eye overview of data-intensive supercomputing solutions. For me, the most interesting chapter in this part is chapter 11, which describes the knowledge engineering language (KEL) for graph problems. The chapter is short and aims to provide an overview of concepts, KEL principles, and basic syntax.

The last part of the book, chapters 12 to 15, presents real applications solved on HPCC systems. These applications include cyber security, fraud (both in insurance and in medication prescriptions), network analysis, social networks analysis, and some others.

In the end, I have to say that the book offers a good overview of big data technologies, which keeps a live link between theoretical background and live applications. As such, the text rises up as a starting point for engineers and researchers in the field of big data applications.

Reviewer:  Alexander Tzanov Review #: CR145317 (1708-0511)
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