CRC Press, 2017. — 593 p. — ISBN-13: 9781498768078.Big Data Management and Processing explores a range of big data related issues and their impact on the design of new computing systems. The twenty-one chapters were carefully selected and feature contributions from several outstanding researchers. The book endeavors to strike a balance between theoretical and practical coverage of innovative problem solving techniques for a range of platforms. It serves as a repository of paradigms, technologies, and applications that target different facets of big data computing systems. The first part of the book explores energy and resource management issues, as well as legal compliance and quality management for Big Data. It covers In-Memory computing and In-Memory data grids, as well as co-scheduling for high performance computing applications. The second part of the book includes comprehensive coverage of Hadoop and Spark, along with security, privacy, and trust challenges and solutions. The latter part of the book covers mining and clustering in Big Data, and includes applications in genomics, hospital big data processing, and vehicular cloud computing. The book also analyzes funding for Big Data projects.Contents Big Data: Legal Compliance and Quality Management Energy Management for Green Big Data Centers The Art of In-Memory Computing for Big Data Processing Scheduling Nested Transactions on In-Memory Data Grids Co-Scheduling High-Performance Computing Applications Resource Management for MapReduce Jobs Performing Big Data Analytics Tyche: An Efficient Ethernet-Based Protocol for Converged Networked Storage Parallel Backpropagation Neural Network for Big Data Processing on Many-Core Platform SQL-on-Hadoop Systems: State-of-the-Art Exploration, Models, Performances, Issues, and Recommendations One Platform Rules All: From Hadoop 1.0 to Hadoop 2.0 and Spark Security, Privacy, and Trust for User-Generated Content: The Challenges and Solutions Role of Real-Time Big Data Processing in the Internet of Things End-to-End Security Framework for Big Sensing Data Streams Considerations on the Use of Custom Accelerators for Big Data Analytics Complex Mining from Uncertain Big Data in Distributed Environments: Problems, Definitions, and Two Effective and Efficient Algorithms Clustering in Big Data Large Graph Computing Systems Big Data in Genomics Maximizing the Return on Investment in Big Data Projects: An Approach Based upon the Incremental Funding of Project Development Parallel Data Mining and Applications in Hospital Big Data Processing Big Data in the Parking Lot
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