This edition first published 2017
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Library of Congress Cataloging-in-Publication Data
Names: Hwang, Kai, author. | Chen, Min, author.
Title: Big-Data Analytics for Cloud, IoT and Cognitive Computing/
Kai Hwang, Min Chen.
Description: Chichester, UK ; Hoboken, NJ : John Wiley & Sons, 2017. |
Includes bibliographical references and index.
Identifiers: LCCN 2016054027 (print) | LCCN 2017001217 (ebook) | ISBN
9781119247029 (cloth : alk. paper) | ISBN 9781119247043 (Adobe PDF) | ISBN
9781119247296 (ePub)
Subjects: LCSH: Cloud computing--Data processing. | Big data.
Classification: LCC QA76.585 .H829 2017 (print) | LCC QA76.585 (ebook) | DDC
004.67/82--dc23
LC record available at https://lccn.loc.gov/2016054027
Cover Design: Wiley
Cover Images: (Top Inset Image) © violetkaipa/Shutterstock;(Bottom Inset Image) © 3alexd/Gettyimages;(Background Image) © adventtr/Gettyimages
Kai Hwang is Professor of Electrical Engineering and Computer Science at the University of Southern California (USC). He has also served as a visiting Chair Professor at Tsinghua University, Hong Kong University, University of Minnesota and Taiwan University. With a PhD from the University of California, Berkeley, he specializes in computer architecture, parallel processing, wireless Internet, cloud computing, distributed systems and network security. He has published eight books, including Computer Architecture and Parallel Processing (McGraw-Hill 1983) and Advanced Computer Architecture (McGraw-Hill 2010). The American Library Association has named his book: Distributed and Cloud Computing (with Fox and Dongarra) as a 2012 outstanding title published by Morgan Kaufmann. His new book, Cloud Computing for Machine Learning and Cognitive Applications (MIT Press 2017) is a good companion to this book. Dr Hwang has published 260 scientific papers. Google Scholars has cited his published work 16,476 times with an h-index of 54 as of early 2017. An IEEE Life Fellow, he has served as the founding Editor-in-Chief of the Journal of Parallel and Distributed Computing (JPDC) for 28 years.
Dr Hwang has served on the editorial boards of IEEE Transactions on Cloud Computing (TCC), Parallel and Distributed Systems (TPDS), Service Computing (TSC) and the Journal of Big Data Intelligence. He has received the Lifetime Achievement Award from IEEE CloudCom 2012 and the Founder's Award from IEEE IPDPS 2011. He received the 2004 Outstanding Achievement Award from China Computer Federation (CCF). Over the years, he has produced 21 PhD students at USC and Purdue University, four of them elevated to IEEE Fellows and one an IBM Fellow. He has chaired numerous international conferences and delivered over 50 keynote speech and distinguished lectures in IEEE/ACM/CCF conferences or at major universities worldwide. He has served as a consultant or visiting scientist for IBM, Intel, Fujitsu Reach Lab, MIT Lincoln Lab, JPL at Caltech, French ENRIA, ITRI in Taiwan, GMD in Germany, and the Chinese Academy of Sciences.
Min Chen is a Professor of Computer Science and Technology at Huazhong University of Science and Technology (HUST), where he serves as the Director of the Embedded and Pervasive Computing (EPIC) Laboratory. He has chaired the IEEE Computer Society Special Technical Communities on Big Data. He was on the faculty of the School of Computer Science and Engineering at Seoul National University from 2009 to 2012. Prior to that, he has worked as a postdoctoral fellow in the Department of Electrical and Computer Engineering, University of British Columbia for 3 years.
Dr Chen received Best Paper Award from IEEE ICC 2012. He is a Guest Editor for IEEE Network, IEEE Wireless Communications Magazine, etc. He has published 260 papers including 150+ SCI-indexed papers. He has 20 ESI highly cited or hot papers. He has published the book: OPNET IoT Simulation (2015) and Software Defined 5G Networks (2016) with HUST Press, and another book on Big Data Related Technologies (2014) in the Springer Series in Computer Science. As of early 2017, Google Scholars cited his published work over 8,350 times with an h-index of 45. His top paper was cited more than 900 times. He has been an IEEE Senior Member since 2009. His research focuses on the Internet of Things, Mobile Cloud, Body Area Networks, Emotion-aware Computing, Healthcare Big Data, Cyber Physical Systems, and Robotics.
In the past decade, the computer and information industry has experienced rapid changes in both platform scale and scope of applications. Computers, smart phones, clouds and social networks demand not only high performance but also a high degree of machine intelligence. In fact, we are entering an era of big data analysis and cognitive computing. This trendy movement is observed by the pervasive use of mobile phones, storage and computing clouds, revival of artificial intelligence in practice, extended supercomputer applications, and widespread deployment of Internet of Things (IoT) platforms. To face these new computing and communication paradigm, we must upgrade the cloud and IoT ecosystems with new capabilities such as machine learning, IoT sensing, data analytics, and cognitive power that can mimic or augment human intelligence.
In the big data era, successful cloud systems, web services and data centers must be designed to store, process, learn and analyze big data to discover new knowledge or make critical decisions. The purpose is to build up a big data industry to provide cognitive services to offset human shortcomings in handling labor-intensive tasks with high efficiency. These goals are achieved through hardware virtualization, machine learning, deep learning, IoT sensing, data analytics, and cognitive computing. For example, new cloud services appear as Learning as a Services (LaaS), Analytics as a Service (AaaS), or Security as a Service (SaaS), along with the growing practices of machine learning and data analytics.
Today, IT companies, big enterprises, universities and governments are mostly converting their data centers into cloud facilities to support mobile and networked applications. Supercomputers having a similar cluster architecture as clouds are also under transformation to deal with the large data sets or streams. Smart clouds become greatly on demand to support social, media, mobile, business and government operations. Supercomputers and cloud platforms have different ecosystems and programming environments. The gap between them must close up towards big data computing in the future. This book attempts to achieve this goal.
The book consists of eight Chapters, presented in a logic flow of three technical parts. The three parts should be read or taught in a sequence, entirely or selectively.
To promote effective big data computing on smart clouds or supercomputers, we take a technological fusion approach by integrating big data theories with cloud design principles and supercomputing standards. The IoT sensing enables large data collection. Machine learning and data analytics help decision-making. Augmenting clouds and supercomputers with artificial intelligence (AI) features is our fundamental goal. These AI and machine learning tasks are supported by Hadoop, Spark and TensorFlow programming libraries in real-life applications.
The book material is based on the authors' research and teaching experiences over the years. It will benefit those who leverage their computer, analytical and application skills to push for career development, business transformation and scientific discovery in the big data world. This book blends big data theories with emerging technologies on smart clouds and exploring distributed datacenters with new applications. Today, we see cyber physical systems appearing in smart cities, autonomous car driving on the roads, emotion-detection robotics, virtual reality, augmented reality and cognitive services in everyday life.
The data analysts, cognitive scientists and computer professionals must work together to solve practical problems. This collaborative learning must involve clouds, mobile devices, datacenters and IoT resources. The ultimate goal is to discover new knowledge, or make important decisions, intelligently. For many years, we have wanted to build brain-like computers that can mimic or augment human functions in sensing, memory, recognition and comprehension. Today, Google, IBM, Microsoft, the Chinese Academy of Science, and Facebook are all exploring AI in cloud and IoT applications.
Some new neuromorphic chips and software platforms are now built by leading research centers to enable cognitive computing. We will examine these advances in hardware, software and ecosystems. The book emphasizes not only machine learning in pattern recognition, speech/image understanding, language translation and comprehension, with low cost and power requirements, but also the emerging new approaches in building future computers.
One example is to build a small rescue robotic system that can automatically distinguish between voices in a meeting and create accurate transcripts for each speaker. Smart computers or cloud systems should be able to recognize faces, detect emotions, and even may be able to issue tsunami alerts or predict earthquakes and severe weather conditions, more accurately and timely. We will cover these and related topics in the three logical parts of the book: systems, algorithms and applications. To close up the application gaps between clouds and big data user groups, over 100 illustrative examples are given to emphasize the strong collaboration among professionals working in different areas.
To serve the best interest of our readers, we write this book to meet the growing demand of the updated curriculum in Computer Science and Electrical Engineering education. By teaching various subsets of nine chapters, instructors can use the book at both senior and graduate levels. Four university courses may adopt this book in the subject areas of Big Data Analytics (BD), Cloud Computing (CC), Machine Learning (ML) and Cognitive Systems (CS). Readers could also use the book as a major reference. The suggested course offerings are growing rapidly at major universities throughout the world. Logically, the reading of the book should follow the order of the three parts.
The book will also benefit computer professionals who wish to transform their skills to meet new IT challenges. For examples, interested readers may include Intel engineers working on Cloud of Things. Google brain and DeepMind teams develop machine learning services including autonomic vehicle driving. Facebook explores new AI features, social and entertainment services based on AV/VR (augmented and virtual realities) technology. IBM clients expect to push cognitive computing services in the business and social-media world. Buyers and sellers on Amazon and Alibaba clouds may want to expand their on-line transaction experiences with many other forms of e-commerce and social services.
Instructors can teach only selected chapters that match their own expertise and serve the best interest of students at appropriate levels. To teach in each individual subject area (BD, CC, ML and CS), each course covers 6 to 7 chapters as suggested below:
Instructors can also choose to offer a course to cover the union of two subject areas such as in the following 3 combinations.
Solutions Manual and PowerPoint slides will be made available to instructors who wish to use the material for classroom use. The website materials will be available in late 2017.
Big-Data Analytics for Cloud, IoT and Cognitive Computing is accompanied by a website:
www.wiley.com/go/hwangIOT
The website includes: