Here’s a sample of IEEE e-books that focus on big data and its many applications. To view the books, log in to the IEEE Xplore Digital Library and click on “Books & eBooks” in the left-hand navigation menu. You can then browse or search by title and download a PDF of an excerpt or of selected chapters. To order books, visit Wiley.com.
Techniques for Surviving the Mobile Data Explosion
By Dinesh Chandra Verma and Paridhi Verma (2014)
An in-depth resource that tackles how to handle the rapid growth of mobile data with limited bandwidth capacity. Chapters cover ways to support data from mobile devices, an overview of methods for bandwidth optimization, techniques for cost reduction, and more.
The Data Bonanza: Improving Knowledge Discovery in Science, Engineering, and Business
Edited by Malcolm Atkinson et al. (2013)
A guide for using data-intensive systems and extracting information from them. The material highlights problem-solving strategies when working with big data and offers case studies of successful applications in astronomy, transportation, and other fields.
Business and Scientific Workflows: A Web Service-Oriented Approach
By Wei Tan and MengChu Zhou (2013)
A look at how to design, analyze, and deploy Web workflows for business and scientific applications in health care and biomedicine, including DNA-sequence data processing. The book covers data-driven composition rules, network analysis, and system architectures for personalized health care.
A compendium on best practices for accessing and using the vast amounts of multimedia data available over large networks such as Flickr and YouTube, as well as from surveillance cameras, TV and radio news, and industry and government media collections. Written by an international team of experts, this book was the first not only to address retrieval of media but also to include information about tools that can mine and index images and videos.
Data Mining: Concepts, Models, Methods, and Algorithms (Second Edition)
By Mehmed Kantardzic (2011)
An introductory guide to the analysis of large data sets, Data Mining highlights techniques for analyzing raw data to make better decisions. It describes how to work with complex software tools, and it includes review questions and exercises at the end of each chapter.