CRC Press Smart Data: State-of-the-Art Perspectives in Computing and Applications (Chapman & Hall/CRC Big Data Series)

Smart Data: State-of-the-Art Perspectives in Computing and Applications explores smart data computing techniques to provide intelligent decision making and prediction services support for business, science, and engineering. It also examines the latest research trends in fields related to smart data computing and applications, including new computing theories, data mining and machine learning techniques. The book features contributions from leading experts and covers cutting-edge topics such as smart data and cloud computing, AI for networking, smart data deep learning, Big Data capture and representation, AI for Big Data applications, and more. Features Presents state-of-the-art research in big data and smart computing Provides a broad coverage of topics in data science and machine learning Combines computing methods with domain knowledge and a focus on applications in science, engineering, and business Covers data security and privacy, including AI techniques Includes contributions from leading researchers Review "The book is a great resource for all those interested in learning about the interplay between "smart data" and a number of research themes: algorithms, applications, ethical issues, security and data privacy, among many other topics. The book will be very useful for researchers, practitioners, and graduate students; a welcome addition to the Smart Data literature. The book is a milestone in this fast-moving field." ― Professor Albert Y. Zomaya, Sydney University, Australia "This book provides a precious set of high-level research contributions for learning how to extract Smart Data from Big Data. Novel intelligent techniques, models, algorithms and applications are presented and discussed. Young researchers and professionals will benefit from the book contents to learn new topics and investigate new research issues. From infrastructure and algorithms to applications and ethics, Smart Data are illustrated and put in practice." ― Professor Domenico Talia, Università della Calabria, Italy About the Author Kuan-Ching Li is a Distinguished Professor of Computer Science and Engineering at Providence University, Taiwan. He is a recipient of guest and distinguished chair professorships from universities in China and other countries, and awards and funding support from a number of agencies and industrial companies. He has been actively involved in many major conferences and workshops in program/general/steering conference chairman positions, and has organized numerous conferences related to highperformance computing and computational science and engineering. He is a Fellow of IET, senior member of the IEEE and a member of the AAAS, Editor-in-Chief of International Journal of Computational Science and Engineering (IJCSE), International Journal of Embedded Systems (IJES), and International Journal of High Performance Computing and Networking (IJHPCN), published by Inderscience. Besides publication of journal and conference research papers, he is co-author/co-editor of several technical professional books published by CRC Press, Springer, McGraw-Hill and IGI Global. His research interests include GPU/many-core computing, Big Data, and Cloud. Beniamino DiMartino is Full Professor at the University of Campania (Italy). He is author of 14 international books and more than 300 publications in international journals and conferences; has been Coordinator of EU funded FP7-ICT Project mOSAIC, and participates to various international research projects; is Editor / Associate Editor of seven international journals and EB Member of several international journals; is vice Chair of the Executive Board of the IEEE CS Technical Committee on Scalable Computing; is member of: IEEE WG for the IEEE P3203 Standard on Cloud Interoperability, IEEE Intercloud Testbed Initiative, IEEE Technical Committees on Scalable Computing (TCSC) and on Big Data (TCBD), Cloud Standards Customer Council, Cloud Co