Big Data and Differential Privacy

Nii Attoh-Okine, PhD, PE is Professor in the Department of Civil and Environmental Engineering at the University of Delaware. The author of over 70 journal articles, his main areas of research include big data and data science; computational intelligence; graphical models and belief functions; civil infrastructure systems; image and signal processing; resilience engineering; and railway track analysis. Dr. Attoh-Okine has edited five books in the areas of computational intelligence, infrastructure systems and has served as an Associate Editor of various ASCE and IEEE journals.

• Preface xiAcknowledgments xiii1 Introduction 11.1 General 11.2 Track Components 21.3 Characteristics of Railway Track Data 41.4 Railway Track Engineering Problems 61.5 Wheel–Rail Interface Data 111.6 Geometry Data 151.7 Track Geometry DegradationModels 201.8 Rail Defect Data 251.9 Inspection and Detection Systems 331.10 Rail Grinding 371.11 Traditional Data Analysis Techniques 401.12 Remarks 41References 422 Data Analysis – Basic Overview 492.1 Introduction 492.2 Exploratory Data Analysis (EDA) 492.3 Symbolic Data Analysis 532.4 Imputation 542.5 Bayesian Methods and Big Data Analysis 562.6 Remarks 57References 573 Machine Learning: A Basic Overview 593.1 Introduction 593.2 Supervised Learning 603.3 Unsupervised Learning 613.4 Semi-Supervised Learning 613.5 Reinforcement Learning 613.6 Data Integration 633.7 Data Science Ontology 633.8 Imbalanced Classification 693.9 Model Validation 703.10 Ensemble Methods 713.11 Big P and Small N (P â N) 743.12 Deep Learning 793.13 Data Stream Processing 953.14 Remarks 105References 1054 Basic Foundations of Big Data 1134.1 Introduction 1134.2 Query 1164.3 Taxonomy of Big Data Analytics in Railway Track Engineering 1234.4 Data Engineering 1244.5 Remarks 130References 1305 Hilbert–Huang Transform, Profile, Signal, and Image Analysis 1335.1 Hilbert–Huang Transform 1335.2 Axle Box Acceleration 1505.3 Analysis 1515.4 Remarks 153References 1536 Tensors – Big Data in Multidimensional Settings 1576.1 Introduction 1576.2 Notations and Definitions 1586.3 Tensor Decomposition Models 1616.4 Application 1646.5 Remarks 170References 1717 Copula Models 1757.1 Introduction 1757.2 Pair Copula: Vines 1847.3 Computational Example 1867.4 Remarks 192References 1938 Topological Data Analysis 1978.1 Introduction 1978.2 Basic Ideas 1978.3 A Simple Railway Track Engineering Application 2038.4 Remarks 204References 2049 Bayesian Analysis 2079.1 Introduction 2079.2 Markov Chain Monte Carlo (MCMC) 2109.3 Approximate Bayesian Computation 2109.4 Markov Chain Monte Carlo Application 2169.5 ABC Application 2199.6 Remarks 221References 22210 Basic Bayesian Nonparametrics 22510.1 General 22510.2 Dirichlet Family 22610.3 Dirichlet Process 22710.4 Finite Mixture Modeling 23110.5 Bayesian Nonparametric Railway Track 23210.6 Remarks 233References 23311 Basic Metaheuristics 23511.1 Introduction 23511.2 Remarks 237References 23912 Differential Privacy 24112.1 General 24112.2 Differential Privacy 24212.3 Remarks 247References 247Index 249