Big Data Management and Processing

From the Foreword:

“Big Data Management and Processing

is [a] state-of-the-art book that deals with a wide range of topical themes in the field of Big Data. The book, which probes many issues related to this exciting and rapidly growing field, covers processing, management, analytics, and applications… [It] is a very valuable addition to the literature. It will serve as a source of up-to-date research in this continuously developing area. The book also provides an opportunity for researchers to explore the use of advanced computing technologies and their impact on enhancing our capabilities to conduct more sophisticated studies.”

—Sartaj Sahni, University of Florida, USA

“Big Data Management and Processing covers the latest Big Data research results in processing, analytics, management and applications. Both fundamental insights and representative applications are provided. This book is a timely and valuable resource for students, researchers and seasoned practitioners in Big Data fields.

–Hai Jin, Huazhong University of Science and Technology, China

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.

Table of Contents

Chapter 1 Big Data: Legal Compliance and Quality Management
Chapter 2 Energy Management for Green Big Data Centers
Chapter 3 The Art of In-Memory Computing for Big Data Processing
Chapter 4 Scheduling Nested Transactions on In-Memory Data Grids
Chapter 5 Co-Scheduling High-Performance Computing Applications
Chapter 6 Resource Management for MapReduce Jobs Performing Big Data Analytics
Chapter 7 Tyche: An Efficient Ethernet-Based Protocol for Converged Networked Storage
Chapter 8 Parallel Backpropagation Neural Network for Big Data Processing on Many-Core Platform
Chapter 9 SQL-on-Hadoop Systems: State-of-the-Art Exploration, Models, Performances, Issues, and Recommendations
Chapter 10 One Platform Rules All: From Hadoop 1.0 to Hadoop 2.0 and Spark
Chapter 11 Security, Privacy, and Trust for User-Generated Content: The Challenges and Solutions
Chapter 12 Role of Real-Time Big Data Processing in the Internet of Things
Chapter 13 End-to-End Security Framework for Big Sensing Data Streams
Chapter 14 Considerations on the Use of Custom Accelerators for Big Data Analytics
Chapter 15 Complex Mining from Uncertain Big Data in Distributed Environments: Problems, Definitions, and Two Effective and Efficient Algorithms
Chapter 16 Clustering in Big Data
Chapter 17 Large Graph Computing Systems
Chapter 18 Big Data in Genomics
Chapter 19 Maximizing the Return on Investment in Big Data Projects: An Approach Based upon the Incremental Funding of Project Development
Chapter 20 Parallel Data Mining and Applications in Hospital Big Data Processing
Chapter 21 Big Data in the Parking Lot