© 2016

Big Data Technologies and Applications


Table of contents

  1. Front Matter
    Pages i-xviii
  2. Big Data Technologies

    1. Front Matter
      Pages 1-1
    2. Borko Furht, Flavio Villanustre
      Pages 3-11
    3. Chun-Wei Tsai, Chin-Feng Lai, Han-Chieh Chao, Athanasios V. Vasilakos
      Pages 13-52
    4. Karl Weiss, Taghi M. Khoshgoftaar, DingDing Wang
      Pages 53-99
    5. Ekaterina Olshannikova, Aleksandr Ometov, Yevgeni Koucheryavy, Thomas Olsson
      Pages 101-131
    6. Maryam M. Najafabadi, Flavio Villanustre, Taghi M. Khoshgoftaar, Naeem Seliya, Randall Wald, Edin Muharemagc
      Pages 133-156
  3. LexisNexis Risk Solution to Big Data

    1. Front Matter
      Pages 157-157
    2. Anthony M. Middleton, David Alan Bayliss, Gavin Halliday, Arjuna Chala, Borko Furht
      Pages 159-183
    3. Anthony M. Middleton, David Bayliss, Bob Foreman
      Pages 185-223
    4. David Bayliss
      Pages 225-235
    5. David Bayliss
      Pages 237-255
    6. Anthony M. Middleton
      Pages 257-306
    7. David Bayliss, Flavio Villanustre
      Pages 307-328
  4. Big Data Applications

    1. Front Matter
      Pages 329-329
    2. Flavio Villanustre, Mauricio Renzi
      Pages 331-339
    3. Flavio Villanustre, Borko Furht
      Pages 341-346
    4. Jesse Shaw, Flavio Villanustre, Borko Furht, Ankur Agarwal, Abhishek Jain
      Pages 347-385
    5. I. Itauma, M. S. Aslan, X. W. Chen, Flavio Villanustre
      Pages 387-400

About this book


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 Big data applications HPCC systems Big data analytics Big data components Visualization of big data Models of big data Social network analytics ECL language Big data software Machine learning techniques Deep learning techniques Data security and privacy Data intensive supercomputing

Authors and affiliations

  1. 1.Department of Computer Science and EngineeringFlorida Atlantic UniversityBoca RatonUSA
  2. 2.LexisNexis Risk SolutionsAlpharettaUSA

About the authors

Dr. Borko Furht is a professor in the Department of Electrical & Computer Engineering and Computer Science at Florida Atlantic University (FAU) in Boca Raton, Florida. He is also Director of the NSF Industry/University Cooperative Research Center on Advanced Knowledge Enablement. Before joining FAU, he was a vice president of research and a senior director of development at Modcomp (Ft. Lauderdale), a computer company of Daimler Benz, Germany, a professor at University of Miami in Coral Gables, Florida, and a senior researcher in the Institute Boris Kidric-Vinca, Yugoslavia. Professor Furht received the Ph.D. degree in electrical and computer engineering from the University of Belgrade. His current research is in multimedia systems, multimedia big data and its applications, 3D video and image systems, wireless multimedia, and Internet and cloud computing. He is presently Principal Investigator and Co-PI of several projects sponsored by NSF and various high-tech companies. He is the author of numerous books and articles in the areas of multimedia, data-intensive applications, computer architecture, real-time computing, and operating systems. He is a founder and editor-in-chief of two journals: Journal of Big Data and Journal of Multimedia Tools and Applications. He has received several technical and publishing awards, and has consulted for many high-tech companies including IBM, Hewlett-Packard, Adobe, Xerox, General Electric, JPL, NASA, Honeywell, and RCA. He has also served as a consultant to various colleges and universities. He has given many invited talks, keynote lectures, seminars, and tutorials. He served on the Board of Directors of several high-tech companies.

333px;">Dr. Flavio Villanustre leads HPCC Systems®, and is also VP, Technology for LexisNexis Risk Solutions®. In this position, he is responsible for Information and Physical Security, overall platform strategy and new product development. Dr. Villanustre is also involved in a number of projects involving Big Data integration, analytics and Business Intelligence. Previously, Dr. Villanustre was Director of Infrastructure for Seisint. Prior to 2001, he served in a variety of roles at different companies including Infrastructure, Information Security and Information Technology. In addition to this, Dr. Villanustre has been involved with the open source community for over 15 years through multiple initiatives. Some of these include founding the first Linux User Group in Buenos Aires (BALUG) in 1994, releasing several pieces of software under different open source licenses, and evangelizing open source to different audiences through conferences, training and education. Prior to his technology career, Dr. Villanustre was a neurosurgeon.

Bibliographic information


“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)