This volume offers readers various perspectives and visions for cutting-edge research in ubiquitous healthcare. The topics emphasize large-scale architectures and high performance solutions for smart healthcare, healthcare monitoring using large-scale computing techniques, Internet of Things (IoT) and big data analytics for healthcare, Fog Computing, mobile health, large-scale medical data mining, advanced machine learning methods for mining multidimensional sensor data, smart homes, and resource allocation methods for the BANs. The book contains high quality chapters contributed by leading international researchers working in domains, such as e-Health, pervasive and context-aware computing, cloud, grid, cluster, and big-data computing. We are optimistic that the topics included in this book will provide a multidisciplinary research platform to the researchers, practitioners, and students from biomedical engineering, health informatics, computer science, and computer engineering.

First designed to generate personalized recommendations to users in the 90s, recommender systems apply knowledge discovery techniques to users' data to suggest information, products, and services that best match their preferences. In recent decades, we have seen an exponential increase in the volumes of data, which has introduced many new challenges. Divided into two volumes, this comprehensive set covers recent advances, challenges, novel solutions, and applications in big data recommender systems. Volume 1 contains 14 chapters addressing foundations, algorithms and architectures, approaches for big data, and trust and security measures. Volume 2 covers a broad range of application paradigms for recommender systems over 22 chapters.

The fields of Big Data and the Internet of Things (IoT) have seen tremendous advances, developments, and growth in recent years. The IoT is the inter-networking of connected smart devices, buildings, vehicles and other items which are embedded with electronics, software, sensors and actuators, and network connectivity that enable these objects to collect and exchange data. The IoT produces a lot of data. Big data describes very large and complex data sets that traditional data processing application software is inadequate to deal with, and the use of analytical methods to extract value from data. This edited book covers analytical techniques for handling the huge amount of data generated by the Internet of Things, from architectures and platforms to security and privacy issues, applications, and challenges as well as future directions.

First designed to generate personalized recommendations to users in the 90s, recommender systems apply knowledge discovery techniques to users' data to suggest information, products, and services that best match their preferences. In recent decades, we have seen an exponential increase in the volumes of data, which has introduced many new challenges. Divided into two volumes, this comprehensive set covers recent advances, challenges, novel solutions, and applications in big data recommender systems. Volume 2 covers a broad range of application paradigms for recommender systems over 22 chapters. Volume 1 contains 14 chapters addressing foundations, algorithms and architectures, approaches for big data, and trust and security measures.

This handbook offers a comprehensive review of the state-of-the-art research achievements in the field of data centers. Contributions from international, leading researchers and scholars offer topics in cloud computing, virtualization in data centers, energy efficient data centers, and next generation data center architecture.  It also comprises current research trends in emerging areas, such as data security, data protection management, and network resource management in data centers. Specific attention is devoted to industry needs associated with the challenges faced by data centers, such as various power, cooling, floor space, and associated environmental health and safety issues, while still working to support growth without disrupting quality of service. The contributions cut across various IT data technology domains as a single source to discuss the interdependencies that need to be supported to enable a virtualized, next-generation, energy efficient, economical, and environmentally friendly data center. This book appeals to a broad spectrum of readers, including server, storage, networking, database, and applications analysts, administrators, and architects. It is intended for those seeking to gain a stronger grasp on data center networks: the fundamental protocol used by the applications and the network, the typical network technologies, and their design aspects. The Handbook of Data Centers is a leading reference on design and implementation for planning, implementing, and operating data center networks.