Zhu Han, Mingyi Hong, Dan WangCambridge University Press, 4/27/2017EAN 9781107124387, ISBN10: 1107124387Hardcover, 474 pages, 25.3 x 17.9 x 2.2 cmLanguage: EnglishThis 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 Applications1. Introduction2. Data parallelismthe supporting architecturePart II. Methodology and Mathematical Background3. First order methods4. Sparse optimization5. Sublinear algorithms6. Tensor for big data7. Deep learning and applicationsPart III. Big Data Applications8. Compressive sensing based big data analysis9. Distributed large-scale optimization10. Optimization of finite sums11. Big data optimization for communication networks12. Big data optimization for smart grid systems13. Processing large data set in MapReduce14. Massive data collection using wireless sensor networks.
Related
Related
Related
Related