Anand Paul

The authors provide an understanding of big data and MapReduce by clearly presenting the basic terminologies and concepts. They have employed over 100 illustrations and many worked-out examples to convey the concepts and methods used in big data, the inner workings of MapReduce, and single node/multi-node installation on physical/virtual machines. This book covers almost all the necessary information on Hadoop MapReduce for most online certification exams. Upon completing this book, readers will find it easy to understand other big data processing tools such as Spark, Storm, etc. Ultimately, readers will be able to:• understand what big data is and the factors that are involved• understand the inner workings of MapReduce, which is essential for certification exams• learn the features and weaknesses of MapReduce• set up Hadoop clusters with 100s of physical/virtual machines• create a virtual machine in AWS• write MapReduce with Eclipse in a simple way• understand other big data processing tools and their applications

The authors provide an understanding of big data and MapReduce by clearly presenting the basic terminologies and concepts. They have employed over 100 illustrations and many worked-out examples to convey the concepts and methods used in big data, the inner workings of MapReduce, and single node/multi-node installation on physical/virtual machines. This book covers almost all the necessary information on Hadoop MapReduce for most online certification exams. Upon completing this book, readers will find it easy to understand other big data processing tools such as Spark, Storm, etc. Ultimately, readers will be able to:• understand what big data is and the factors that are involved• understand the inner workings of MapReduce, which is essential for certification exams• learn the features and weaknesses of MapReduce• set up Hadoop clusters with 100s of physical/virtual machines• create a virtual machine in AWS• write MapReduce with Eclipse in a simple way• understand other big data processing tools and their applications

The expansion of digital data has transformed various sectors of business such as healthcare, industrial manufacturing, and transportation. A new way of solving business problems has emerged through the use of machine learning techniques in conjunction with big data analytics. Deep Learning Innovations and Their Convergence With Big Data is a pivotal reference for the latest scholarly research on upcoming trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. Featuring extensive coverage on a broad range of topics and perspectives such as deep neural network, domain adaptation modeling, and threat detection, this book is ideally designed for researchers, professionals, and students seeking current research on the latest trends in the field of deep learning techniques in big data analytics. Contents include:Deep Auto-EncodersDeep Neural NetworkDomain Adaptation ModelingMultilayer Perceptron (MLP)Natural Language Processing (NLP)Restricted Boltzmann Machines (RBM)Threat Detection

Intelligent Vehicular Network and Communications: Fundamentals, Architectures and Solutions begins with discussions on how the transportation system has transformed into today’s Intelligent Transportation System (ITS). It explores the design goals, challenges, and frameworks for modeling an ITS network, discussing vehicular network model technologies, mobility management architectures, and routing mechanisms and protocols. It looks at the Internet of Vehicles, the vehicular cloud, and vehicular network security and privacy issues. The book investigates cooperative vehicular systems, a promising solution for addressing current and future traffic safety needs, also exploring cooperative cognitive intelligence, with special attention to spectral efficiency, spectral scarcity, and high mobility. In addition, users will find a thorough examination of experimental work in such areas as Controller Area Network protocol and working function of On Board Unit, as well as working principles of roadside unit and other infrastructural nodes. Finally, the book examines big data in vehicular networks, exploring various business models, application scenarios, and real-time analytics, concluding with a look at autonomous vehicles.