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Furht B., Villanustre F. Big Data Technologies and Applications

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Furht B., Villanustre F. Big Data Technologies and Applications
Springer International Publishing, Switzerland, 2016. — 405 p. — ISBN 3319445480.
The objective of this book is to introduce the basic concepts of big data computing and then to describe the total solution of big data problems using HPCC, an open-source computing platform.
The book comprises 15 chapters broken into three parts. The first part, Big Data Technologies, includes introductions to big data concepts and techniques; big data analytics; and visualization and learning techniques. The second part, LexisNexis Risk Solution to Big Data, focuses on specific 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. The third part, Big Data Applications, describes various data intensive applications solved on HPCC Systems. It includes applications such as cyber security, social network analytics including fraud, Ebola spread modeling using big data analytics, unsupervised learning, and image classification.
The book is intended for a wide variety of people including researchers, scientists, programmers, engineers, designers, developers, educators, and students. This book can also be beneficial for business managers, entrepreneurs, and investors.
Big Data Technologies
Introduction to Big Data
Big Data Analytics
Transfer Learning Techniques
Visualizing Big Data
Deep Learning Techniques in Big Data Analytics
LexisNexis Risk Solution to Big Data
The HPCC/ECL Platform for Big Data
Scalable Automated Linking Technology for Big Data Computing
Aggregated Data Analysis in HPCC Systems
Models for Big Data
Data Intensive Supercomputing Solutions
Graph Processing with Massive Datasets: A Kel Primer
Big Data Applications
HPCC Systems for Cyber Security Analytics
Social Network Analytics: Hidden and Complex Fraud Schemes
Modeling Ebola Spread and Using HPCC/KEL System
Unsupervised Learning and Image Classification in High Performance Computing Cluster
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