This book introduces two basic big data processing paradigms for batch data and streaming data. Representative programming frameworks are also presented, as well as software defined networking (SDN) and network function virtualization (NFV) technologies as key cloud networking technologies. The authors illustrate that SDN and NFV can be applied to benefit the big data processing by proposing a cloud networking framework. Based on the framework, two case studies examine how to improve the cost efficiency of big data processing. Cloud Networking for Big Data targets professionals and researchers working in big data, networks, wireless communications and information technology. Advanced-level students studying computer science and electrical engineering will also find this book valuable as a study guide.

With new technologies, such as computer vision, internet of things, mobile computing, e-governance and e-commerce, and wide applications of social media, organizations generate a huge volume of data and at a much faster rate than several years ago. Big data in large-/small-scale systems, characterized by high volume, diversity, and velocity, increasingly drives decision making and is changing the landscape of business intelligence. From governments to private organizations, from communities to individuals, all areas are being affected by this shift. There is a high demand for big data analytics that offer insights for computing efficiency, knowledge discovery, problem solving, and event prediction. To handle this demand and this increase in big data, there needs to be research on innovative and optimized machine learning algorithms in both large- and small-scale systems. Applications of Big Data in Large- and Small-Scale Systems includes state-of-the-art research findings on the latest development, up-to-date issues, and challenges in the field of big data and presents the latest innovative and intelligent applications related to big data. This book encompasses big data in various multidisciplinary fields from the medical field to agriculture, business research, and smart cities. While highlighting topics including machine learning, cloud computing, data visualization, and more, this book is a valuable reference tool for computer scientists, data scientists and analysts, engineers, practitioners, stakeholders, researchers, academicians, and students interested in the versatile and innovative use of big data in both large-scale and small-scale systems.

It is essential for an organization to know before involving themselves in cloud computing and big data, what are the key security requirements for applications and data processing. Big data and cloud computing are integrated together in practice. Cloud computing offers massive storage, high computation power, and distributed capability to support processing of big data. In such an integrated environment the security and privacy concerns involved in both technologies become combined. This book discusses these security and privacy issues in detail and provides necessary insights into cloud computing and big data integration. It will be useful in enhancing the body of knowledge concerning innovative technologies offered by the research community in the area of cloud computing and big data. Readers can get a better understanding of the basics of cloud computing, big data, and security mitigation techniques to deal with current challenges as well as future research opportunities.

This book discusses intelligent computing through the Internet of Things (IoT) and Big-Data in vehicular environments in a single volume. It covers important topics, such as topology-based routing protocols, heterogeneous wireless networks, security risks, software-defined vehicular ad-hoc networks, vehicular delay tolerant networks, and energy harvesting for WSNs using rectenna. FEATURES Covers applications of IoT in Vehicular Ad-hoc Networks (VANETs) Discusses use of machine learning and other computing techniques for enhancing performance of networks Explains game theory-based vertical handoffs in heterogeneous wireless networks Examines monitoring and surveillance of vehicles through the vehicular sensor network Investigates theoretical approaches on software-defined VANET The book is aimed at graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science, and engineering.

Vehicular traffic congestion and accidents remain universal issues in today’s world. Due to the continued growth in the use of vehicles, optimizing traffic management operations is an immense challenge. To reduce the number of traffic accidents, improve the performance of transportation systems, enhance road safety, and protect the environment, vehicular ad-hoc networks have been introduced. Current developments in wireless communication, computing paradigms, big data, and cloud computing enable the enhancement of these networks, equipped with wireless communication capabilities and high-performance processing tools. Cloud-Based Big Data Analytics in Vehicular Ad-Hoc Networks is a pivotal reference source that provides vital research on cloud and data analytic applications in intelligent transportation systems. While highlighting topics such as location routing, accident detection, and data warehousing, this publication addresses future challenges in vehicular ad-hoc networks and presents viable solutions. This book is ideally designed for researchers, computer scientists, engineers, automobile industry professionals, IT practitioners, academicians, and students seeking current research on cloud computing models in vehicular networks.

This book is a compendium of the proceedings of the International Conference on Big-Data and Cloud Computing. It includes recent advances in the areas of big data analytics, cloud computing, the Internet of nano things, cloud security, data analytics in the cloud, smart cities and grids, etc. Primarily focusing on the application of knowledge that promotes ideas for solving the problems of the society through cutting-edge technologies, it provides novel ideas that further world-class research and development. This concise compilation of articles approved by a panel of expert reviewers is an invaluable resource for researchers in the area of advanced engineering sciences.

Clouds are being positioned as the next-generation consolidated, centralized, yet federated IT infrastructure for hosting all kinds of IT platforms and for deploying, maintaining, and managing a wider variety of personal, as well as professional applications and services. Handbook of Research on Cloud Infrastructures for Big Data Analytics focuses exclusively on the topic of cloud-sponsored big data analytics for creating flexible and futuristic organizations. This book helps researchers and practitioners, as well as business entrepreneurs, to make informed decisions and consider appropriate action to simplify and streamline the arduous journey towards smarter enterprises.