Signal Processing and Networking for Big Data Applications

This unique text helps make sense of big data using signal processing techniques, in applications including machine learning, networking, and energy systems.

Zhu Han (Author), Mingyi Hong (Author), Dan Wang (Author)

9781107124387, Cambridge University Press

Hardback, published 27 April 2017

474 pages, 91 b/w illus. 11 tables
25.3 x 17.9 x 2.2 cm, 0.89 kg

'A very nice balanced treatment over two large-scale signal processing aspects: mathematical backgrounds versus big data applications, with a strong flavor of distributed optimization and computation.' Shuguang Cui, University of California, Davis

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.

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.

Subject Areas: Signal processing [UYS], Communications engineering / telecommunications [TJK], Information theory [GPF]