Springer High-Performance Big-Data Analytics: Computing Systems and Approaches (Computer Communications and Networks)

This book presents a detailed review of high-performance computing infrastructures for next-generation big data and fast data analytics. Features: includes case studies and learning activities throughout the book and self-study exercises in every chapter; presents detailed case studies on social media analytics for intelligent businesses and on big data analytics (BDA) in the healthcare sector; describes the network infrastructure requirements for effective transfer of big data, and the storage infrastructure requirements of applications which generate big data; examines real-time analytics solutions; introduces in-database processing and in-memory analytics techniques for data mining; discusses the use of mainframes for handling real-time big data and the latest types of data management systems for BDA; provides information on the use of cluster, grid and cloud computing systems for BDA; reviews the peer-to-peer techniques and tools and the common information visualization techniques, used in BDA. From the Back Cover This important and timely text/reference presents a detailed review of high-performance computing infrastructures for next-generation big data and fast data analytics.  Comprehensively covering a diverse range of computer systems and proven techniques for high-performance big-data analytics, the book also presents case studies, practical guidelines, and best practices for enabling decision-making toward implementing the appropriate computer systems and approaches. Topics and features: Includes case studies and learning activities throughout the book, and self-study exercises at the end of every chapter Presents detailed case studies on social media analytics for intelligent businesses, and on big data analytics in the healthcare sector Describes the network infrastructure requirements for effective transfer of big data, and the storage infrastructure requirements of applications which generate big data Examines real-time analytics solutions, such as machine data analytics and operational analytics Introduces in-database processing and in-memory analytics techniques for data mining Discusses the use of mainframes for handling real-time big data, and the latest types of data management systems for big and fast data analytics Provides information on the use of cluster, grid and cloud computing systems for big data analytics and data-intensive processing Reviews the peer-to-peer techniques and tools, and the common information visualization techniques, used in big data analytics Software engineers, cloud professionals and big data scientists will find this book to be an informative and inspiring read, highlighting the indispensable role data analytics will play in shaping a smart future.