Preface
The scope of this book includes leading edge in big data systems, architectures, and
applications. Big data computing refers to capturing, managing, analyzing, and
understanding the data at volumes and rates that push the frontiers of current
technologies. The challenge of big data computing is to provide the hardware
architectures and related software systems and techniques which are capable of
transforming ultra large data into valuable know ledge. Big data and data-intensive
computing demand a fundamentally different set of principles than mainstream
computing. Big data applications typically are well suited for large-scale parallelism
over the data and also require extremely high degree of fault tolerance, reliability,
and availability. In addition, most big data applications require relatively fast
response. The objective of this book is to introduce the basic concepts of big data
computing and then to describe the total solution to big data problems developed by
LexisNexis Risk Solutions.
This book comprises of three parts, which consists of 15 chapters. Part I on Big
Data Technologies includes the chapters dealing with introduction to big data
concepts and techniques, big data analytics and relating platforms, and visualization
techniques and deep learning techniques for big data. Part II on LexisNexis Risk
Solution to Big Data focuses on speci fic technologies and techniques developed at
LexisNexis to solve critical problems that use big data analytics. It covers the open
source high performance computing cluster (HPCC Systems
®
) platform and its
architecture, as well as, parallel data languages ECL and KEL, developed to
effectively solve big data problems. Part III on Big Data Applications describes
various data-intensive applications solved on HPCC Systems. It includes applica-
tions such as cyber security, social network analytics, including insurance fraud,
fraud in prescription drugs, and fraud in Medicaid, and others. Other HPCC
Systems applications described include Ebola spread modeling using big data
analytics and unsupervised learning and image classification.
With the dramatic growth of data-intensive computing and systems and big data
analytics, this book can be the definitive resource for persons working in this field
as researchers, scientists, programmers, engineers, and users. This book is intended
for a wide variety of people including academicians, designers, developers,
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