Includes bibliographical references and index.

Part I. Overview of Big Data Applications: 1. Introduction; 2. Data parallelism: the supporting architecture; Part II. Methodology and Mathematical Background: 3. First order methods; 4. Sparse optimization; 5. Sublinear algorithms; 6. Tensor for big data; 7. Deep learning and applications; Part III. Big Data Applications: 8. Compressive sensing based big data analysis; 9. Distributed large-scale optimization; 10. Optimization of finite sums; 11. Big data optimization for communication networks; 12. Big data optimization for smart grid systems; 13. Processing large data set in MapReduce; 14. Massive data collection using wireless sensor networks.

This unique text helps make sense of big data in engineering applications using tools and techniques from signal processing. It presents fundamental signal processing theories and software implementations, reviews current research trends and challenges, and describes the techniques used for analysis, design and optimization. Readers will learn about key theoretical issues such as data modelling and representation, scalable and low-complexity information processing and optimization, tensor and sublinear algorithms, and deep learning and software architecture, and their application to a wide range of engineering scenarios. Applications discussed in detail include wireless networking, smart grid systems, and sensor networks and cloud computing. This is the ideal text for researchers and practising engineers wanting to solve practical problems involving large amounts of data, and for students looking to grasp the fundamentals of big data analytics.