Although there are already some books published on Big Data, most of them only cover basic concepts and society impacts and ignore the internal implementation details—making them unsuitable to R&D people. To fill such a need, Big Data: Storage, Sharing, and Security examines Big Data management from an R&D perspective. It covers the 3S designs—storage, sharing, and security—through detailed descriptions of Big Data concepts and implementations.
Written by well-recognized Big Data experts around the world, the book contains more than 450 pages of technical details on the most important implementation aspects regarding Big Data. After reading this book, you will understand how to:
- Aggregate heterogeneous types of data from numerous sources, and then use efficient database management technology to store the Big Data
- Use cloud computing to share the Big Data among large groups of people
- Protect the privacy of Big Data during network sharing
With the goal of facilitating the scientific research and engineering design of Big Data systems, the book consists of two parts. Part I, Big Data Management, addresses the important topics of spatial management, data transfer, and data processing. Part II, Security and Privacy Issues, provides technical details on security, privacy, and accountability.
Examining the state of the art of Big Data over clouds, the book presents a novel architecture for achieving reliability, availability, and security for services running on the clouds. It supplies technical descriptions of Big Data models, algorithms, and implementations, and considers the emerging developments in Big Data applications. Each chapter includes references for further study.
Table of Contents
Big Data Management: Storage, Sharing, and Processing. Challenges and Approaches in Spatial Big Data Management. Storage and Database Management for Big Data. Performance Evaluation of Protocols for Big Data Transfers. Challenges in Crawling the Deep Web. Big Data and Information Distillation in Social Sensing. Big Data and the SP Theory of Intelligence. A Qualitatively Different Principle for the Organization of Big Data Processing. Big Data Security: Security, Privacy, and Accountability. Integration with Cloud Computing Security. Toward Reliable and Secure Data Access for Big Data Service. Cryptography for Big Data Security. Some Issues of Privacy in a World of Big Data and Data Mining. Privacy in Big Data. Privacy and Integrity of Outsourced Data Storage and Processing. Privacy and Accountability Concerns in the Age of Big Data. Secure Outsourcing of Data Analysis. Composite Big Data Modeling for Security Analytics. Exploring the Potential of Big Data for Malware Detection and Mitigation Techniques in Android Environment. Index.
Fei Hu is a professor of electrical and computer engineering at the University of Alabama. Dr. Hu is the author of 10 books and over 200 articles published in top journals and conferences. His current research interests include big data security and 5G networks.