UID:
almafu_9960117732302883
Format:
1 online resource (xii, 362 pages) :
,
digital, PDF file(s).
ISBN:
1-108-15549-9
,
1-108-15654-1
,
1-316-40803-5
Content:
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.
Note:
Title from publisher's bibliographic system (viewed on 25 May 2017).
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Cover -- Half-title page -- Reviews -- Title page -- Copyright page -- Dedication page -- Contents -- Part I Overview of Big Data Applications -- 1 Introduction -- 1.1 Background -- 1.2 Market and Readership -- 1.3 Brief Description of the Chapters -- 1.4 Acknowledgments -- 2 Data Parallelism: The Supporting Architecture -- 2.1 Data Parallelism -- 2.2 Hadoop Distributed File System (HDFS) -- 2.3 MapReduce and Yarn -- 2.3.1 MapReduce -- 2.3.2 Yarn -- 2.4 Spark -- 2.5 GraphLab and Spark Streaming -- 2.5.1 GraphLab -- 2.5.2 Spark Streaming -- 2.6 Big Data in the Cloud -- 2.7 Advances in the Data Parallelism Architecture -- 2.7.1 Fast Job Completion Time -- 2.7.2 Data-Computation Contention -- 2.7.3 Optimizing Resource Utilization for Big Data Jobs -- 2.8 Supports for Signal Processing Algorithms -- 2.9 Summary -- Part II Methodology and Mathematical Background -- 3 First-Order Methods -- 3.1 A Brief Introduction to Optimization -- 3.1.1 Definition and Notations -- 3.1.2 Unconstrained Optimization -- 3.1.3 Constrained Optimization -- 3.2 Gradient Descent Method -- 3.3 Block Coordinate Descent Method -- 3.4 Block Successive Upper-Bound Minimization Method -- 3.5 The Augmented Lagrangian Method -- 3.6 Alternating Direction Method of Multipliers -- 3.6.1 The Algorithm -- 3.6.2 Convergence Analysis -- 3.6.3 Solving Multiblock Problems and Beyond -- 3.7 Conclusion -- 4 Sparse Optimization -- 4.1 Sparse Optimization Models -- 4.2 Classic Solvers -- 4.3 The BSUM Method for Sparse Optimization -- 4.4 Shrinkage Operation -- 4.4.1 Generalizations of Shrinkage -- 4.5 Prox-Linear Algorithms -- 4.5.1 Forward-Backward Operator Splitting -- 4.5.2 Examples -- 4.5.3 Convergence Rates -- 4.6 Dual Algorithms -- 4.6.1 Dual Formulations -- 4.7 Bregman Method -- 4.7.1 Bregman Iterations and Denoising -- 4.7.2 Linearized Bregman and Augmented Models.
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4.7.3 Handling Complex Data and Variables -- 4.8 ADMM for Sparse Optimization -- 4.9 Homotopy Algorithms and Parametric Quadratic Programming -- 4.10 Continuation, Varying Stepsizes, and Line Search -- 4.11 Greedy Algorithms -- 4.11.1 Greedy Pursuit Algorithms -- 4.11.2 Iterative Support Detection -- 4.11.3 Hard Thresholding -- 4.12 Algorithms for Low-Rank Matrices -- 4.13 How to Choose an Algorithm -- 5 Sublinear Algorithms -- 5.1 Introduction -- 5.1.1 Organization -- 5.2 Foundations -- 5.2.1 Approximation and Randomization -- 5.2.2 Inequalities and Bounds -- 5.2.3 Classification of Sublinear Algorithms -- 5.3 Examples -- 5.3.1 Estimating the User Percentage: The Very First Example -- 5.3.2 Finding Distinct Elements -- 5.3.3 Computing the Number of Connected Components -- 5.3.4 The Two-Cat Problem -- 5.4 Summary -- 6 Tensor for Big Data -- 6.1 Tensor Basics -- 6.1.1 Tensor Definitions -- 6.1.2 Tensor Operations -- 6.2 Tensor Decomposition -- 6.2.1 Tucker3 and PARAFAC -- 6.2.2 Tensor Compression -- 6.2.3 Tensor Completion -- 6.3 Tensor Voting -- 6.3.1 Encode Normal Space with Tensor -- 6.3.2 Inferring Structure -- 6.3.3 Token Refinement -- 6.3.4 Token Decomposition -- 6.3.5 Inference Algorithm -- 6.4 Other Tensor Applications -- 7 Deep Learning and Applications -- 7.1 Introduction -- 7.1.1 History -- 7.1.2 Milestone Literature -- 7.1.3 Applications -- 7.2 Deep Learning Basics -- 7.2.1 Restricted Boltzmann Machine -- 7.2.2 Stochastic Gradient Descent and Backpropagation -- 7.2.3 Deep Learning Networks -- 7.3 Example 1: Apache Spark for MBD -- 7.3.1 Introduction -- 7.3.2 MBD Concepts and Features -- 7.3.3 Deep Learning in MBD Analytics -- 7.3.4 Spark-Based Deep Learning Framework for MBD -- 7.3.5 Prototyping a Context-Aware Activity Recognition Systems -- 7.3.6 Future Work -- 7.4 Example 2: User Moving Pattern Extraction -- 7.4.1 Introduction.
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7.4.2 Data Collection -- 7.4.3 Deep Learning Extraction -- 7.4.4 Experiment Results -- 7.5 Example 3: Combination with Nonparametric Bayesian Learning -- 7.5.1 Introduction -- 7.5.2 Generative Model -- 7.5.3 Inference Algorithm -- 7.5.4 Simulations and Discussions for Wireless Applications -- 7.6 Summary -- Part III Big Data Applications -- 8 Compressive Sensing-Based Big Data Analysis -- 8.1 Background -- 8.1.1 Steps of Compressive Sensing -- 8.1.2 Robust Compressive Sensing and Signal Recovery -- 8.2 Traditional Sensing vs. Compressive Sensing -- 8.3 Sparse Representation -- 8.3.1 Extensions of Sparse Models -- 8.4 CS Encoding and Decoding -- 8.4.1 The Null-Space Property (NSP) -- 8.4.2 The Restricted Isometry Principle (RIP) -- 8.4.3 The Spherical Section Property -- 8.4.4 "RIPless'' Analysis -- 8.4.5 Non-ℓ[sub(1)] Decoding Methods -- 8.4.6 Examples -- 8.5 Compressive Sensing-Based Big Data Applications -- 8.5.1 CS-Based Analog-to-Digital Converter -- 8.5.2 Communications -- 8.5.3 Hyperspectral Imaging -- 8.5.4 Data Streaming -- 9 Distributed Large-Scale Optimization -- 9.1 Background -- 9.1.1 Problem Formulation -- 9.1.2 Applications -- 9.2 Distributed Gradient/Subgradient Methods -- 9.2.1 Algorithm Description -- 9.2.2 Convergence Analysis and Variants -- 9.2.3 Convergence Rate Analysis -- 9.3 ADMM-Based Methods -- 9.3.1 Problem Formulation -- 9.3.2 Distributed Implementation -- 9.3.3 Convergence and Variations -- 9.4 Other Types of First-Order Method -- 9.5 Special Case: Global Consensus Problem -- 9.6 Comparing Different Algorithms -- 9.6.1 Problem Types -- 9.6.2 Graph Types -- 9.6.3 Convergence Rates -- 10 Optimization of Finite Sums -- 10.1 The Incremental Gradient Algorithms -- 10.2 The Stochastic Gradient-Based Algorithms -- 10.2.1 The SAG-Based Method -- 10.2.2 The SVRG Algorithm -- 10.3 The Stochastic Algorithms in the Dual.
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10.4 Summary -- 11 Big Data Optimization for Communication Networks -- 11.1 Mobile Data Offloading in Software-Defined Networks -- 11.2 Scalable Service Management in Mobile Cloud Computing -- 11.2.1 Literature Review -- 11.2.2 System Model and Problem Formulation -- 11.2.3 Mobile Cloud Service Management -- 11.2.4 Maximizing Total Revenue -- 11.2.5 Numerical Results -- 11.3 Scalable Workload Management in Data Centers -- 11.3.1 Problem Formulation -- 11.4 Resource Allocation for Wireless Network Virtualization -- 11.4.1 Wireless Network Virtualization -- 11.4.2 Routing Model for Virtual Network -- 11.4.3 Joint Resource and Routing Optimization for Wireless Virtual Networks -- 11.5 Summary -- 12 Big Data Optimization for Smart Grid Systems -- 12.1 Introduction -- 12.2 Backgrounds -- 12.2.1 False Data Injection Attacks against State Estimation -- 12.2.2 Security-Constrained Optimal Power Flow -- 12.3 Sparse Optimization for False Data Injection Detection -- 12.3.1 Sparse Optimization Problem Formulation -- 12.3.2 Nuclear Norm Minimization -- 12.3.3 Low-Rank Matrix Factorization -- 12.3.4 Numerical Results -- 12.4 Distributed Parallel Approach for Security-Constrained Optimal Power Flow -- 12.4.1 An Introduction to ADMM -- 12.4.2 Distributed and Parallel Approach for SCOPF -- 12.4.3 Numerical Results -- 12.5 Concluding Remarks -- 13 Processing Large Data Sets in MapReduce -- 13.1 Introduction -- 13.1.1 The Data Skew Problem of MapReduce Jobs -- 13.1.2 Chapter Outline -- 13.2 Server Load Balancing: Analysis and Problem Formulation -- 13.2.1 Background and Motivation -- 13.2.2 Problem Formulation -- 13.2.3 Input Models -- 13.3 A 2-Competitive Fully Online Algorithm -- 13.4 A Sampling-Based Semi-Online Algorithm -- 13.4.1 Sample Size -- 13.4.2 Heavy Keys -- 13.4.3 A Sample-Based Algorithm -- 13.5 Performance Evaluation -- 13.5.1 Simulation Setup.
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13.5.2 Results on Synthetic Data -- 13.5.3 Results on Real Data -- 13.6 Summary -- 14 Massive Data Collection Using Wireless Sensor Networks -- 14.1 Introduction -- 14.1.1 Background and Related Work -- 14.1.2 Chapter Outline -- 14.2 System Architecture -- 14.2.1 Preliminaries -- 14.2.2 Network Construction -- 14.2.3 Specifying the Structure of the Layers -- 14.2.4 Data Collection and Aggregation -- 14.3 Evaluation of the Accuracy and the Number of Sensors Queried -- 14.3.1 MAX and MIN Queries -- 14.3.2 QUANTILE Queries -- 14.3.3 AVERAGE and SUM Queries -- 14.3.4 Effect of the Promotion Probability p -- 14.4 Energy Consumption -- 14.4.1 Overall Lifetime of the System -- 14.5 Evaluation Results -- 14.5.1 System Settings -- 14.5.2 Layers vs. Accuracy -- 14.6 Practical Variations of the Architecture -- 14.7 Summary -- Bibliography -- Index.
Additional Edition:
ISBN 1-107-12438-7
Language:
English
URL:
https://doi.org/10.1017/9781316408032
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