Big data in engineering applications
Edited book
| Book Title | Big data in engineering applications |
|---|---|
| Book Category | Edited book |
| ERA Publisher ID | 3337 |
| Editors | Roy, Sanjiban Sekhar, Samui, Pijushi, Deo, Ravinesh and Ntalampiras, Stalampiras |
| Series | Studies in Big Data |
| Year | 2018 |
| Publisher | Springer |
| Place of Publication | Singapore |
| ISBN | 9789811084751 |
| 9789811084768 | |
| ISSN | 2197-6503 |
| 2197-6511 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/978-981-10-8476-8 |
| Web Address (URL) | http://www.springer.com/us/book/9789811084751#aboutBook |
| Abstract | 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. |
| ANZSRC Field of Research 2020 | 460510. Recommender systems |
| 469999. Other information and computing sciences not elsewhere classified | |
| 460207. Modelling and simulation | |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Institution of Origin | University of Southern Queensland |
| Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
https://research.usq.edu.au/item/q48z1/big-data-in-engineering-applications
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