文件名称：Big.Data.Technologies.and.Applications
文件大小：8.41MB
文件格式：PDF
更新时间：2019-12-28 05:03:25
Big Data Technologies Applications
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. Table of Contents Chapter 1 Introduction to Big Data Chapter 2 Big Data Analytics Chapter 3 Transfer Learning Techniques Chapter 4 Visualizing Big Data Chapter 5 Deep Learning Techniques in Big Data Analytics Chapter LexisNexis Risk Solution to Big Data Chapter 6 The HPCC/ECL Platform for Big Data Chapter 7 Scalable Automated Linking Technology for Big Data Computing Chapter 8 Aggregated Data Analysis in HPCC Systems Chapter 9 Models for Big Data Chapter 10 Data Intensive Supercomputing Solutions Chapter 11 Graph Processing with Massive Datasets: A Kel Primer Chapter Big Data Applications Chapter 12 HPCC Systems for Cyber Security Analytics Chapter 13 Social Network Analytics: Hidden and Complex Fraud Schemes Chapter 14 Modeling Ebola Spread and Using HPCC/KEL System Chapter 15 Unsupervised Learning and Image Classification in High Performance Computing Cluster