Big Data in Engineering Applications

  • Sanjiban Sekhar Roy
  • Pijush Samui
  • Ravinesh Deo
  • Stavros Ntalampiras

Part of the Studies in Big Data book series (SBD, volume 44)

Table of contents

  1. Front Matter
    Pages i-vi
  2. Jens Brandenburger, Valentina Colla, Silvia Cateni, Antonella Vignali, Floriano Ferro, Christoph Schirm et al.
    Pages 1-20
  3. Minrui Zheng, Wenwu Tang, Yu Lan, Xiang Zhao, Meijuan Jia, Craig Allan et al.
    Pages 21-39
  4. Alejandro Vera-Baquero, Ricardo Colomo-Palacios
    Pages 41-63
  5. Marjana Prifti Skënduli, Marenglen Biba, Michelangelo Ceci
    Pages 65-81
  6. Ankur Saxena, Shivani Singh, Chetna Shakya
    Pages 83-111
  7. R. Vinayakumar, Prabaharan Poornachandran, K. P. Soman
    Pages 113-142
  8. Margarita Ramírez Ramírez, Hilda Beatriz Ramírez Moreno, Esperanza Manrique Rojas
    Pages 143-159
  9. Dianwei Han, Ankit Agrawal, Wei-keng Liao, Alok Choudhary
    Pages 173-192
  10. Taiwo Adetiloye, Anjali Awasthi
    Pages 265-278
  11. Wengang Zhang, Anthony T. C. Goh
    Pages 279-301
  12. Ozgur Kisi, Jalal Shiri, Sepideh Karimi, Rana Muhammad Adnan
    Pages 303-321
  13. Lasyamayee Garanayak, Sarat Kumar Das, Ranajeet Mohanty
    Pages 323-346
  14. Sajad Madadi, Morteza Nazari-Heris, Behnam Mohammadi-Ivatloo, Sajjad Tohidi
    Pages 347-362
  15. Raid Lafta, Ji Zhang, Xiaohui Tao, Yan Li, Mohammed Diykh, Jerry Chun-Wei Lin
    Pages 363-384

About this book


This book presents the current trends, technologies, and challenges in Big Data in the diversified field of engineering and sciences. It covers the applications of Big Data ranging from conventional fields of mechanical engineering, civil engineering to electronics, electrical, and computer science to areas in pharmaceutical and biological sciences. This book consists of contributions from various authors from all sectors of academia and industries, demonstrating the imperative application of Big Data for the decision-making process in sectors where the volume, variety, and velocity of information keep increasing. The book is a useful reference for graduate students, researchers and scientists interested in exploring the potential of Big Data in the application of engineering areas.


Deep Learning Machine Learning Deep Neutral Network Data Analytics Predictive Modeling Predictive Analysis Data Visualization Data Mining Internet of Things Decision-Making Process

Editors and affiliations

  • Sanjiban Sekhar Roy
    • 1
  • Pijush Samui
    • 2
  • Ravinesh Deo
    • 3
  • Stavros Ntalampiras
    • 4
  1. 1.School of Computing Science and EngineeringVellore Institute of TechnologyVelloreIndia
  2. 2.Department of Civil EngineeringNational Institute of Technology PatnaPatnaIndia
  3. 3.University of Southern QueenslandSpringfieldAustralia
  4. 4.Polytechnic University of MilanMilanItaly

Bibliographic information

  • DOI
  • Copyright Information Springer Nature Singapore Pte Ltd. 2018
  • Publisher Name Springer, Singapore
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-981-10-8475-1
  • Online ISBN 978-981-10-8476-8
  • Series Print ISSN 2197-6503
  • Series Online ISSN 2197-6511
  • Buy this book on publisher's site