About the Book
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
Table of Contents:
Foreword, ix
Acknowledgement, xi
Editors, xiii
List of Contributors, xv
CHAPTER 1 ■ Extreme Heterogeneity in Deep Learning Architectures 1
JEFF ANDERSON, ARMIN MEHRABIAN, JIAXIN PENG, AND TAREK EL-GHAZAWI
CHAPTER 2 ■ GPU PaaS Computation Model in Aneka Cloud
Computing Environments 19
SHASHIKANT ILAGER, RAJEEV WANKAR, RAGHAVENDRA KUNE, AND RAJKUMAR BUYYA
CHAPTER 3 ■ Toward Complex Search for Encrypted Mobile Cloud
Data via Index Blind Storage 41
YUPENG HU, LINJUN WU, WENJIA LI, KEQIN LI, YONGHE LIU, AND ZHENG QIN
CHAPTER 4 ■ Encrypted Big Data Deduplication in Cloud Storage 63
ZHENG YAN, XUEQIN LIANG, WENXIU DING, XIXUN YU, MINGJUN WANG, AND
ROBERT H. DENG
CHAPTER 5 ■ The Role of NonSQL Databases in Big Data 93
ANTONIO SARASA CABEZUELO
CHAPTER 6 ■ Prescriptive and Predictive Analytics Techniques for
Enabling Cybersecurity 113
NITIN SUKHIJA, SONNY SEVIN, ELIZABETH BAUTISTA, AND DAVID DAMPIER
CHAPTER 7 ■ Multivariate Projection Techniques to Reduce
Dimensionality in Large Datasets 133
I. BARRANCO CHAMORRO, S. MUÑOZ-ARMAYONES, A. ROMERO-LOSADA,
AND F. ROMERO-CAMPERO
CHAPTER 8 ■ Geo-Distributed Big Data Analytics Systems: An
Online Learning Approach for Dynamic Deployment 161
YIXIN BAO AND CHUAN WU
CHAPTER 9 ■ The Role of Smart Data in Inference of Human Behavior
and Interaction 191
RUTE C. SOFIA, LILIANA CARVALHO, AND FRANCISCO M. PEREIRA
CHAPTER 10 ■ Compression of Wearable Body Sensor Network Data 215
ROBINSON RAJU, MELODY MOH, AND TENG-SHENG MOH
CHAPTER 11 ■ Population-Specific and Personalized (PSP) Models of
Human Behavior for Leveraging Smart and
Connected Data 243
THEODORA CHASPARI, ADELA C. TIMMONS, AND GAYLA MARGOLIN
CHAPTER 12 ■ Detecting Singular Data for Better Analysis of
Emotional Tweets 259
KIICHI TAGO, KENICHI ITO, AND QUN JIN
CHAPTER 13 ■ Smart Data Infrastructure for Respiratory Health
Protection of Citizens against PM2.5 in Urban Areas 273
DANIEL DUNEA, STEFANIA IORDACHE, ALIN POHOATA, AND EMIL LUNGU
CHAPTER 14 ■ Fog-Assisted Cloud Platforms for Big Data Analytics in
Cyber Physical Systems: A Smart Grid Case Study 289
MD. MUZAKKIR HUSSAIN, MOHAMMAD SAAD ALAM, AND M.M. SUFYAN BEG
CHAPTER 15 ■ When Big Data and Data Science Prefigured Ambient
Intelligence 319
CHRISTOPHE THOVEX
CHAPTER 16 ■ Ethical Issues and Considerations of Big Data 343
EDWARD T. CHEN
CHAPTER 17 ■ Data Protection by Design in Smart Data Environments 359
PAOLO BALBONI
INDEX, 391
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 Computing Experts' Group of the European Commission.
Dr. Laurence T. Yang is a professor and W.F. James Research Chair at St. Francis Xavier University, Canada. His research includes parallel and distributed computing, embedded systems/internet of things, ubiquitous/pervasive computing and intelligence, and big data. He has published around 400 international journal papers in the above areas, of which half are on top IEEE/ACM Transactions and Journals, others ar mainly on Elsevier, Springer and Wiley Journals. He has been involved actively act as a steering chair for 10+ IEEE international conferences. Now he is the chair of IEEE CS Technical Committee of Scalable Computing (2018-), the chair of IEEE SMC Technical Committee on Cybermatics (2016-). He is also serving as an editor for many international journals (such as IEEE Systems Journal, IEEE Access, Future Generation of Computer Systems (Elsevier), Information Sciences (Elsevier), Information Fusion (Elsevier), Big Data Research (Elsevier), etc). He is an elected fellow of Canadian Academy of Engineering (CAE) and Engineering Institute of Canada (EIC).
Dr. Zhang is an Assistant Professor at St. Francis Xavier University, Canada. His research interests include big data, machine learning, and smart medicine. He has published more than 20 top international journal papers on the above topics including papers in IEEE Transactions on Computers, IEEE Transactions on Services Computing, ACM Multimedia Computing, Communications and Applications, and so on. He got an IEEE TCSC Award for Excellence in Scalable Computing for Early Career Researchers in 2018. He served as vice chair of IEEE Canada Atlantic Section CIS/SMC joint chapter (2018-2019). He served as a program chair of IEEE 14th International Conference on Pervasive, Intelligence and Computing (PICom 2016) and IEEE 11th International Conference on Internet of Things (iThings 2018). In addition, he is one of the guest editors of several international journals such as Future Generation Computer Systems, IEEE Access and Wireless Communication and Mobile Computing.
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
