Signal Processing and Networking for Big Data Applications

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.

Table of Contents

Part I  Overview of Big Data Applications
Chapter 1. Introduction
Chapter 2. Data Parallelism: The Supporting Architecture

Part II  Methodology and Mathematical Background
Chapter 3. First-Order Methods
Chapter 4. Sparse Optimization
Chapter 5. Sublinear Algorithms
Chapter 6. Tensor For Big Data
Chapter 7. Deep Learning And Applications

Part III  Big Data Applications
Chapter 8. Compressive Sensing-Based Big Data Analysis
Chapter 9. Distributed Large-Scale Optimization
Chapter 10. Optimization Of Finite Sums
Chapter 11. Big Data Optimization For Communication Networks
Chapter 12. Big Data Optimization For Smart Grid Systems
Chapter 13. Processing Large Data Sets In Mapreduce
Chapter 14. Massive Data Collection Using Wireless Sensor Networks