Big Data in Engineering Applications
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 e
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Sanjiban Sekhar Roy · Pijush Samui Ravinesh Deo · Stavros Ntalampiras Editors
Big Data in Engineering Applications
Studies in Big Data Volume 44
Series editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland e-mail: [email protected]
The series “Studies in Big Data” (SBD) publishes new developments and advances in the various areas of Big Data- quickly and with a high quality. The intent is to cover the theory, research, development, and applications of Big Data, as embedded in the fields of engineering, computer science, physics, economics and life sciences. The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensors or other physical instruments as well as simulations, crowd sourcing, social networks or other internet transactions, such as emails or video click streams and other. The series contains monographs, lecture notes and edited volumes in Big Data spanning the areas of computational intelligence incl. neural networks, evolutionary computation, soft computing, fuzzy systems, as well as artificial intelligence, data mining, modern statistics and Operations research, as well as self-organizing systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. ** Indexing: The books of this series are submitted to ISI Web of Science, DBLP, Ulrichs, MathSciNet, Current Mathematical Publications, Mathematical Reviews, Zentralblatt Math: MetaPress and Springerlink.
More information about this series at http://www.springer.com/series/11970
Sanjiban Sekhar Roy ⋅ Pijush Samui Ravinesh Deo ⋅ Stavros Ntalampiras Editors
Big Data in Engineering Applications
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Editors Sanjiban Sekhar Roy School of Computing Science and Engineering Vellore Institute of Technology Vellore, Tamil Nadu India Pijush Samui Department of Civil Engineering National Institute of Technology Patna Patna, Bihar India
Ravinesh Deo University of Southern Queensland Springfield, QLD Australia Stavros Ntalampiras Polytechnic University of Milan Milan Italy
ISSN 2197-6503 ISSN 2197-6511 (electronic) Studies in Big Data ISBN 978-981-10-8475-1 ISBN 978-981-10-8476-8 (eBook) https://doi.org/10.1007/978-981-10-8476-8 Library of Congress Control Number: 2018935215 © Springer Nature Singapore Pte Ltd. 2018 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply,
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