{"product_id":"big-data-and-differential-privacy-9781119229049","title":"Big Data and Differential Privacy","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eA comprehensive introduction to the theory and practice of contemporary data science analysis for railway track engineering\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFeaturing a practical introduction to state-of-the-art data analysis for railway track engineering, \u003ci\u003eBig Data and Differential Privacy: Analysis Strategies for Railway Track Engineering \u003c\/i\u003eaddresses common issues with the implementation of big data applications while exploring the limitations, advantages, and disadvantages of more conventional methods. In addition, the book provides a unifying approach to analyzing large volumes of data in railway track engineering using an array of proven methods and software technologies.\u003c\/p\u003e \u003cp\u003eDr. Attoh-Okine considers some of today's most notable applications and implementations and highlights when a particular method or algorithm is most appropriate. Throughout, the book presents numerous real-world examples to illustrate the latest railway engineering big data applications of predictive analytics, such a\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003eAcknowledgments xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 General 1\u003c\/p\u003e \u003cp\u003e1.2 Track Components 2\u003c\/p\u003e \u003cp\u003e1.3 Characteristics of Railway Track Data 4\u003c\/p\u003e \u003cp\u003e1.4 Railway Track Engineering Problems 6\u003c\/p\u003e \u003cp\u003e1.5 Wheel–Rail Interface Data 11\u003c\/p\u003e \u003cp\u003e1.6 Geometry Data 15\u003c\/p\u003e \u003cp\u003e1.7 Track Geometry DegradationModels 20\u003c\/p\u003e \u003cp\u003e1.8 Rail Defect Data 25\u003c\/p\u003e \u003cp\u003e1.9 Inspection and Detection Systems 33\u003c\/p\u003e \u003cp\u003e1.10 Rail Grinding 37\u003c\/p\u003e \u003cp\u003e1.11 Traditional Data Analysis Techniques 40\u003c\/p\u003e \u003cp\u003e1.12 Remarks 41\u003c\/p\u003e \u003cp\u003eReferences 42\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Data Analysis – Basic Overview 49\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 49\u003c\/p\u003e \u003cp\u003e2.2 Exploratory Data Analysis (EDA) 49\u003c\/p\u003e \u003cp\u003e2.3 Symbolic Data Analysis 53\u003c\/p\u003e \u003cp\u003e2.4 Imputation 54\u003c\/p\u003e \u003cp\u003e2.5 Bayesian Methods and Big Data Analysis 56\u003c\/p\u003e \u003cp\u003e2.6 Remarks 57\u003c\/p\u003e \u003cp\u003eReferences 57\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Machine Learning: A Basic Overview 59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 59\u003c\/p\u003e \u003cp\u003e3.2 Supervised Learning 60\u003c\/p\u003e \u003cp\u003e3.3 Unsupervised Learning 61\u003c\/p\u003e \u003cp\u003e3.4 Semi-Supervised Learning 61\u003c\/p\u003e \u003cp\u003e3.5 Reinforcement Learning 61\u003c\/p\u003e \u003cp\u003e3.6 Data Integration 63\u003c\/p\u003e \u003cp\u003e3.7 Data Science Ontology 63\u003c\/p\u003e \u003cp\u003e3.8 Imbalanced Classification 69\u003c\/p\u003e \u003cp\u003e3.9 Model Validation 70\u003c\/p\u003e \u003cp\u003e3.10 Ensemble Methods 71\u003c\/p\u003e \u003cp\u003e3.11 Big P and Small N (P â N) 74\u003c\/p\u003e \u003cp\u003e3.12 Deep Learning 79\u003c\/p\u003e \u003cp\u003e3.13 Data Stream Processing 95\u003c\/p\u003e \u003cp\u003e3.14 Remarks 105\u003c\/p\u003e \u003cp\u003eReferences 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Basic Foundations of Big Data 113\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 113\u003c\/p\u003e \u003cp\u003e4.2 Query 116\u003c\/p\u003e \u003cp\u003e4.3 Taxonomy of Big Data Analytics in Railway Track Engineering 123\u003c\/p\u003e \u003cp\u003e4.4 Data Engineering 124\u003c\/p\u003e \u003cp\u003e4.5 Remarks 130\u003c\/p\u003e \u003cp\u003eReferences 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Hilbert–Huang Transform, Profile, Signal, and Image \u003c\/b\u003e\u003cb\u003eAnalysis 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Hilbert–Huang Transform 133\u003c\/p\u003e \u003cp\u003e5.2 Axle Box Acceleration 150\u003c\/p\u003e \u003cp\u003e5.3 Analysis 151\u003c\/p\u003e \u003cp\u003e5.4 Remarks 153\u003c\/p\u003e \u003cp\u003eReferences 153\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Tensors – Big Data in Multidimensional Settings 157\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 157\u003c\/p\u003e \u003cp\u003e6.2 Notations and Definitions 158\u003c\/p\u003e \u003cp\u003e6.3 Tensor Decomposition Models 161\u003c\/p\u003e \u003cp\u003e6.4 Application 164\u003c\/p\u003e \u003cp\u003e6.5 Remarks 170\u003c\/p\u003e \u003cp\u003eReferences 171\u003c\/p\u003e \u003cp\u003e7 Copula Models 175\u003c\/p\u003e \u003cp\u003e7.1 Introduction 175\u003c\/p\u003e \u003cp\u003e7.2 Pair Copula: Vines 184\u003c\/p\u003e \u003cp\u003e7.3 Computational Example 186\u003c\/p\u003e \u003cp\u003e7.4 Remarks 192\u003c\/p\u003e \u003cp\u003eReferences 193\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Topological Data Analysis 197\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 197\u003c\/p\u003e \u003cp\u003e8.2 Basic Ideas 197\u003c\/p\u003e \u003cp\u003e8.3 A Simple Railway Track Engineering Application 203\u003c\/p\u003e \u003cp\u003e8.4 Remarks 204\u003c\/p\u003e \u003cp\u003eReferences 204\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Bayesian Analysis 207\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 207\u003c\/p\u003e \u003cp\u003e9.2 Markov Chain Monte Carlo (MCMC) 210\u003c\/p\u003e \u003cp\u003e9.3 Approximate Bayesian Computation 210\u003c\/p\u003e \u003cp\u003e9.4 Markov Chain Monte Carlo Application 216\u003c\/p\u003e \u003cp\u003e9.5 ABC Application 219\u003c\/p\u003e \u003cp\u003e9.6 Remarks 221\u003c\/p\u003e \u003cp\u003eReferences 222\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Basic Bayesian Nonparametrics 225\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 General 225\u003c\/p\u003e \u003cp\u003e10.2 Dirichlet Family 226\u003c\/p\u003e \u003cp\u003e10.3 Dirichlet Process 227\u003c\/p\u003e \u003cp\u003e10.4 Finite Mixture Modeling 231\u003c\/p\u003e \u003cp\u003e10.5 Bayesian Nonparametric Railway Track 232\u003c\/p\u003e \u003cp\u003e10.6 Remarks 233\u003c\/p\u003e \u003cp\u003eReferences 233\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Basic Metaheuristics 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 235\u003c\/p\u003e \u003cp\u003e11.2 Remarks 237\u003c\/p\u003e \u003cp\u003eReferences 239\u003c\/p\u003e \u003cp\u003e12 Differential Privacy 241\u003c\/p\u003e \u003cp\u003e12.1 General 241\u003c\/p\u003e \u003cp\u003e12.2 Differential Privacy 242\u003c\/p\u003e \u003cp\u003e12.3 Remarks 247\u003c\/p\u003e \u003cp\u003eReferences 247\u003c\/p\u003e \u003cp\u003eIndex 249\u003c\/p\u003e","brand":"John Wiley \u0026 Sons 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