Big Data: Related Technologies, Challenges and Future Prospects (SpringerBriefs in Computer Science) - Tapa blanda

Críticas

“The book not only provides readers with a fundamental understanding of big data, but also points to many future research topics and directions. ... it covers comprehensive aspects of big data and provides abundant references. I believe that it will be a valuable resource for researchers working with big data, especially those working in computer science, computer engineering, or related fields.” (I-Lun Tseng, Computing Reviews, June, 2015)

“This book provides a big picture in this broad research area, covering all the phases of its value chains. ... The book presents an overview on different phrases of big data value chain. ... I would like to recommend this book to researchers and industrial practitioners who work with big data, and it is also useful for researchers in relevant areas such as parallel computing, NoSQL Database, machine learning and data analytics.” (Gang Li, Information Technology and Tourism, Vol. 15, 2015)

Reseña del editor

This Springer Brief provides a comprehensive overview of the background and recent developments of big data. The value chain of big data is divided into four phases: data generation, data acquisition, data storage and data analysis. For each phase, the book introduces the general background, discusses technical challenges and reviews the latest advances. Technologies under discussion include cloud computing, Internet of Things, data centers, Hadoop and more. The authors also explore several representative applications of big data such as enterprise management, online social networks, healthcare and medical applications, collective intelligence and smart grids. This book concludes with a thoughtful discussion of possible research directions and development trends in the field. Big Data: Related Technologies, Challenges and Future Prospects is a concise yet thorough examination of this exciting area. It is designed for researchers and professionals interested in big data or related research. Advanced-level students in computer science and electrical engineering will also find this book useful.