Big Data Technologies and Applications - Furht, Borko

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.?

Biographie:
Nataliia Neshenko, Ph.D., FCCA, PMP, PMI-ACP is Visiting Instructor at the Department of Information Technology and Operations Management (ITOM) at the College of Business, Florida Atlantic University. Her research focus is operational cybersecurity, including attack detection and characterization, risk management methodologies, the Internet of Things, and visual data mining and analysis. Her research findings are published in leading refereed security and data science journals and conferences. Dr. Neshenko is a recipient of the Best Paper Award at the 2017 IEEE International Symposium on Personal Indoor and Mobile Radio Communications. She also has over 16 years of project management experience in the field of business optimization in finance and IT. Bou-Harb, Ph.D., CISSP received a Ph.D. degree in computer science from Concordia University, Montreal, Canada. He was a visiting research scientist at Carnegie Mellon University (CMU), Pittsburgh, PA, USA, in 2015-2016. He is currently the Director of The Cyber Center For Security and Analytics at the University of Texas at San Antonio (UTSA) and an Associate Professor at the Department of Information Systems and Cyber Security. Dr. Bou-Harb specializes in operational cyber security and data science as applicable to the national security challenge. He is also a research scientist at the National Cyber Forensic and Training Alliance (NCFTA) of Canada. His current research interests are operational cyber security, attack detection and characterization, Internet measurements, and cyber security for critical infrastructure. Dr. Bou-Harb has authored more than 120 refereed publications in leading security and data science venues...

Sommaire:
Introduction 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....