| Applications of Big Data in Large- and Small-Scale Systems Contributor(s): Goundar, Sam (Editor), Rayani, Praveen Kumar (Editor) |
|
 |
ISBN: 1799866734 ISBN-13: 9781799866732 Publisher: Engineering Science Reference OUR PRICE: $256.50 Product Type: Hardcover - Other Formats Published: January 2021 |
| Additional Information |
| BISAC Categories: - Computers | Systems Architecture - Distributed Systems & Computing |
| Dewey: 005.7 |
| LCCN: 2020026763 |
| Physical Information: 0.94" H x 8.5" W x 11" (2.75 lbs) 330 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: With new technologies, such as computer vision, internet of things, mobile computing, e-governance and e-commerce, and wide applications of social media, organizations generate a huge volume of data and at a much faster rate than several years ago. Big data in large-/small-scale systems, characterized by high volume, diversity, and velocity, increasingly drives decision making and is changing the landscape of business intelligence. From governments to private organizations, from communities to individuals, all areas are being affected by this shift. There is a high demand for big data analytics that offer insights for computing efficiency, knowledge discovery, problem solving, and event prediction. To handle this demand and this increase in big data, there needs to be research on innovative and optimized machine learning algorithms in both large- and small-scale systems. Applications of Big Data in Large- and Small-Scale Systems includes state-of-the-art research findings on the latest development, up-to-date issues, and challenges in the field of big data and presents the latest innovative and intelligent applications related to big data. This book encompasses big data in various multidisciplinary fields from the medical field to agriculture, business research, and smart cities. While highlighting topics including machine learning, cloud computing, data visualization, and more, this book is a valuable reference tool for computer scientists, data scientists and analysts, engineers, practitioners, stakeholders, researchers, academicians, and students interested in the versatile and innovative use of big data in both large-scale and small-scale systems. |