Big Data and Differential Privacy :

Attoh-Okine, Nii.

Big Data and Differential Privacy : Analysis Strategies for Railway Track Engineering. - 1 online resource (270 pages) - Wiley Series in Operations Research and Management Science Ser. . - Wiley Series in Operations Research and Management Science Ser. .

Cover -- Title Page -- Copyright -- Contents -- Preface -- Acknowledgments -- Chapter 1 Introduction -- 1.1 General -- 1.2 Track Components -- 1.3 Characteristics of Railway Track Data -- 1.4 Railway Track Engineering Problems -- 1.5 Wheel-Rail Interface Data -- 1.5.1 Switches and Crossings -- 1.6 Geometry Data -- 1.7 Track Geometry Degradation Models -- 1.7.1 Deterministic Models -- 1.7.1.1 Linear Models -- 1.7.1.2 Nonlinear Models -- 1.7.2 Stochastic Models -- 1.7.3 Discussion -- 1.8 Rail Defect Data -- 1.9 Inspection and Detection Systems -- 1.10 Rail Grinding -- 1.11 Traditional Data Analysis Techniques -- 1.11.1 Emerging Data Analysis -- 1.12 Remarks -- References -- Chapter 2 Data Analysis - Basic Overview -- 2.1 Introduction -- 2.2 Exploratory Data Analysis (EDA) -- 2.3 Symbolic Data Analysis -- 2.3.1 Building Symbolic Data -- 2.3.2 Advantages of Symbolic Data -- 2.4 Imputation -- 2.5 Bayesian Methods and Big Data Analysis -- 2.6 Remarks -- References -- Chapter 3 Machine Learning: A Basic Overview -- 3.1 Introduction -- 3.2 Supervised Learning -- 3.3 Unsupervised Learning -- 3.4 Semi-Supervised Learning -- 3.5 Reinforcement Learning -- 3.6 Data Integration -- 3.7 Data Science Ontology -- 3.7.1 Kernels -- 3.7.1.1 General -- 3.7.1.2 Learning Process -- 3.7.2 Basic Operations with Kernels -- 3.7.3 Different Kernel Types -- 3.7.4 Intuitive Example -- 3.7.5 Kernel Methods -- 3.7.5.1 Support Vector Machines -- 3.8 Imbalanced Classification -- 3.9 Model Validation -- 3.9.1 Receiver Operating Characteristic (ROC) Curves -- 3.9.1.1 ROC Curves -- 3.10 Ensemble Methods -- 3.10.1 General -- 3.10.2 Bagging -- 3.10.3 Boosting -- 3.11 Big P and Small N (P ≫ N) -- 3.11.1 Bias and Variances -- 3.11.2 Multivariate Adaptive Regression Splines (MARS) -- 3.12 Deep Learning -- 3.12.1 General -- 3.12.2 Deep Belief Networks. 3.12.2.1 Restricted Boltzmann Machines (RBM) -- 3.12.2.2 Deep Belief Nets (DBN) -- 3.12.3 Convolutional Neural Networks (CNN) -- 3.12.4 Granular Computing (Rough Set Theory) -- 3.12.5 Clustering -- 3.12.5.1 Measures of Similarity or Dissimilarity -- 3.12.5.2 Hierarchical Methods -- 3.12.5.3 Non-Hierarchical Clustering -- 3.12.5.4 k-Means Algorithm -- 3.12.5.5 Expectation-Maximization (EM) Algorithms -- 3.13 Data Stream Processing -- 3.13.1 Methods and Analysis -- 3.13.2 LogLog Counting -- 3.13.3 Count-Min Sketch -- 3.13.3.1 Online Support Regression -- 3.14 Remarks -- References -- Chapter 4 Basic Foundations of Big Data -- 4.1 Introduction -- 4.2 Query -- 4.3 Taxonomy of Big Data Analytics in Railway Track Engineering -- 4.4 Data Engineering -- 4.5 Remarks -- References -- Chapter 5 Hilbert-Huang Transform, Profile, Signal, and Image Analysis -- 5.1 Hilbert-Huang Transform -- 5.1.1 Traditional Empirical Mode Decomposition -- 5.1.1.1 Side Effect (Boundary Effect) -- 5.1.1.2 Example -- 5.1.1.3 Stopping Criterion -- 5.1.2 Ensemble Empirical Mode Decomposition (EEMD) -- 5.1.2.1 Post-Processing EEMD -- 5.1.3 Complex Empirical Mode Decomposition (CEMD) -- 5.1.4 Spectral Analysis -- 5.1.5 Bidimensional Empirical Mode Decomposition (BEMD) -- 5.1.5.1 Example -- 5.2 Axle Box Acceleration -- 5.2.1 General -- 5.3 Analysis -- 5.4 Remarks -- References -- Chapter 6 Tensors - Big Data in Multidimensional Settings -- 6.1 Introduction -- 6.2 Notations and Definitions -- 6.3 Tensor Decomposition Models -- 6.3.1 Nonnegative Tensor Factorization -- 6.4 Application -- 6.5 Remarks -- References -- Chapter 7 Copula Models -- 7.1 Introduction -- 7.1.1 Archimedean Copulas -- 7.1.1.1 Concordance Measures -- 7.1.2 Multivariate Archimedean Copulas -- 7.2 Pair Copula: Vines -- 7.3 Computational Example -- 7.3.1 Results -- 7.4 Remarks -- References. Chapter 8 Topological Data Analysis -- 8.1 Introduction -- 8.2 Basic Ideas -- 8.2.1 Topology -- 8.2.2 Homology -- 8.2.2.1 Simplicial Complex -- 8.2.2.2 Cycles, Boundaries, and Homology -- 8.2.3 Persistent Homology -- 8.2.3.1 Filtration -- 8.2.4 Persistence Visualizations -- 8.2.4.1 Persistence Diagrams -- 8.3 A Simple Railway Track Engineering Application -- 8.3.1 Embedding Method -- 8.4 Remarks -- References -- Chapter 9 Bayesian Analysis -- 9.1 Introduction -- 9.1.1 Prior and Posterior Distributions -- 9.2 Markov Chain Monte Carlo (MCMC) -- 9.2.1 Gibbs Sampling -- 9.2.2 Metropolis-Hastings -- 9.3 Approximate Bayesian Computation -- 9.3.1 ABC - Rejection algorithm -- 9.3.2 ABC Steps -- 9.4 Markov Chain Monte Carlo Application -- 9.5 ABC Application -- 9.6 Remarks -- References -- Chapter 10 Basic Bayesian Nonparametrics -- 10.1 General -- 10.2 Dirichlet Family -- 10.2.1 Moments -- 10.2.1.1 Marginal Distribution -- 10.3 Dirichlet Process -- 10.3.1 Stick-Breaking Construction -- 10.3.2 Chinese Restaurant Process -- 10.3.3 Chinese Restaurant Process (CRP) for Infinite Mixture -- 10.3.4 Nonparametric Clustering and Dirichlet Process -- 10.4 Finite Mixture Modeling -- 10.5 Bayesian Nonparametric Railway Track -- 10.6 Remarks -- References -- Chapter 11 Basic Metaheuristics -- 11.1 Introduction -- 11.1.1 Particle Swarm Optimization -- 11.1.2 PSO Algorithm Parameters -- 11.2 Remarks -- References -- Chapter 12 Differential Privacy -- 12.1 General -- 12.2 Differential Privacy -- 12.2.1 Differential Privacy: Hypothetical Track Application -- 12.3 Remarks -- References -- Index -- EULA.

9781119229056


Railroad tracks--Mathematical models.


Electronic books.

TF241.A886 2017

625.14028557