Description
Book SynopsisThe 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.
Trade Review“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.” (Alexander Tzanov, Computing Reviews, June, 2017)
Table of ContentsIntroduction to Big Data.- Big Data Analytics.- Transfer Learning Techniques.- Visualizing Big Data.- Deep Learning and 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.- 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.
