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Big Data and Visual Analytics

  • Conference proceedings
  • © 2017

Overview

Editors:
  1. Sang C. Suh

    1. Department of Computer Science, Texas A&M University-Commerce, Commerce, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Thomas Anthony

    1. Department of Electrical and Computer Engineering, The University of Alabama at Birmingham, Birmingham, USA

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    You can also search for this editor in PubMed   Google Scholar

  • Presents the benefits of the methods and technologies used in big data and visual analytics and examines the challenges of future research in this area
  • Covers tensor-based computation and modeling
  • Each chapter is authored or co-authored by academicians directly involved in research activities delineated in the chapters

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Table of contents (15 papers)

  1. Front Matter

    Pages i-x
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  2. Automated Detection of Central Retinal Vein Occlusion Using Convolutional Neural Network

    • Bismita Choudhury, Patrick H. H. Then, Valliappan Raman
    Pages 1-21

  3. Swarm Intelligence Applied to Big Data Analytics for Rescue Operations with RASEN Sensor Networks

    • U. John Tanik, Yuehua Wang, Serkan Güldal
    Pages 23-54

  4. Gender Classification Based on Deep Learning

    • Dhiraj Gharana, Sang C. Suh, Mingon Kang
    Pages 55-69

  5. Social and Organizational Culture in Korea and Women’s Career Development

    • Choonhee Yang, Yongman Kwon
    Pages 71-84

  6. Big Data Framework for Agile Business (BDFAB) As a Basis for Developing Holistic Strategies in Big Data Adoption

    • Bhuvan Unhelkar
    Pages 85-95

  7. Scalable Gene Sequence Analysis on Spark

    • Muthahar Syed, Taehyun Hwang, Jinoh Kim
    Pages 97-113

  8. Big Sensor Data Acquisition and Archiving with Compression

    • Dongeun Lee
    Pages 115-143

  9. Advanced High Performance Computing for Big Data Local Visual Meaning

    • Ozgur Aksu
    Pages 145-164

  10. Transdisciplinary Benefits of Convergence in Big Data Analytics

    • U. John Tanik, Darrell Fielder
    Pages 165-179

  11. A Big Data Analytics Approach in Medical Imaging Segmentation Using Deep Convolutional Neural Networks

    • Zheng Zhang, David Odaibo, Frank M. Skidmore, Murat M. Tanik
    Pages 181-189

  12. Big Data in Libraries

    • Robert Olendorf, Yan Wang
    Pages 191-202

  13. A Framework for Social Network Sentiment Analysis Using Big Data Analytics

    • Bharat Sri Harsha Karpurapu, Leon Jololian
    Pages 203-217

  14. Big Data Analytics and Visualization: Finance

    • Shyam Prabhakar, Larry Maves
    Pages 219-229

  15. Study of Hardware Trojans in a Closed Loop Control System for an Internet-of-Things Application

    • Ranveer Kumar, Karthikeyan Lingasubramanian
    Pages 231-243

  16. High Performance/Throughput Computing Workflow for a Neuro-Imaging Application: Provenance and Approaches

    • T. Anthony, J. P. Robinson, J. R. Marstrander, G. R. Brook, M. Horton, F. M. Skidmore
    Pages 245-256

  17. Back Matter

    Pages 257-263
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Keywords

About this book

This book provides users with cutting edge methods and technologies in the area of  big data and visual analytics, as well as an insight to the big data and data analytics research conducted by world-renowned researchers in this field. The authors present comprehensive educational resources on big data and visual analytics covering state-of-the art techniques on data analytics, data and information visualization, and visual analytics.

Each chapter covers  specific topics related to big data and data analytics as virtual data machine, security of  big data, big data applications, high performance computing cluster, and big data implementation techniques. Every chapter includes a description of an unique contribution to the area of big data and visual analytics.

This book is a valuable resource for researchers and professionals working in the area of big data, data analytics, and information visualization. Advanced-level students studying computer science will also find this book helpful as a secondary textbook or reference.

Editors and Affiliations

  • Department of Computer Science, Texas A&M University-Commerce, Commerce, USA

    Sang C. Suh

  • Department of Electrical and Computer Engineering, The University of Alabama at Birmingham, Birmingham, USA

    Thomas Anthony

Bibliographic Information

  • Book Title: Big Data and Visual Analytics

  • Editors: Sang C. Suh, Thomas Anthony

  • DOI: https://doi.org/10.1007/978-3-319-63917-8

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer International Publishing AG 2017

  • Hardcover ISBN: 978-3-319-63915-4Published: 17 January 2018

  • Softcover ISBN: 978-3-319-87670-2Published: 04 June 2019

  • eBook ISBN: 978-3-319-63917-8Published: 15 January 2018

  • Edition Number: 1

  • Number of Pages: X, 263

  • Number of Illustrations: 17 b/w illustrations, 10 illustrations in colour

  • Topics: Information Systems and Communication Service, Artificial Intelligence, Visualization

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