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Big data and differential privacy : analysis strategies for railway track engineering /
A comprehensive introduction to the theory and practice of contemporary data science analysis for railway track engineering Featuring a practical introduction to state-of-the-art data analysis for railway track engineering, Big Data and Differential Privacy: Analysis Strategies for Railway Track Eng...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
John Wiley & Sons, Inc.,
2017.
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| Colección: | Wiley series in operations research and management science.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Attoh-Okine, Nii O., |e author. | |
| 245 | 1 | 0 | |a Big data and differential privacy : |b analysis strategies for railway track engineering / |c Nii O. Attoh-Okine. |
| 264 | 1 | |a Hoboken, NJ : |b John Wiley & Sons, Inc., |c 2017. | |
| 300 | |a 1 online resource (xiii, 252 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 490 | 1 | |a Wiley series in operations research and management science | |
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record and CIP data provided by publisher; resource not viewed. | |
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 1.12 RemarksReferences; 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. | |
| 505 | 8 | |a 3.7.2 Basic Operations with Kernels3.7.3 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 k 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). | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 4.3 Taxonomy of Big Data Analytics in Railway Track Engineering4.4 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. | |
| 520 | |a A comprehensive introduction to the theory and practice of contemporary data science analysis for railway track engineering Featuring a practical introduction to state-of-the-art data analysis for railway track engineering, Big Data and Differential Privacy: Analysis Strategies for Railway Track Engineering addresses 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. Dr. 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 as the Union Pacific Railroad's use of big data to reduce train derailments, increase the velocity of shipments, and reduce emissions. In addition to providing an overview of the latest software tools used to analyze the large amount of data obtained by railways, Big Data and Differential Privacy: Analysis Strategies for Railway Track Engineering: - Features a unified framework for handling large volumes of data in railway track engineering using predictive analytics, machine learning, and data mining - Explores issues of big data and differential privacy and discusses the various advantages and disadvantages of more conventional data analysis techniques - Implements big data applications while addressing common issues in railway track maintenance - Explores the advantages and pitfalls of data analysis software such as R and Spark, as well as the Apache Hadoop data collection database and its popular implementation MapReduce Big Data and Differential Privacy is a valuable resource for researchers and professionals in transportation science, railway track engineering, design engineering, operations research, and railway planning and management. The book is also appropriate for graduate courses on data analysis and data mining, transportation science, operations research, and infrastructure management. 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. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Railroad tracks |x Mathematical models. | |
| 650 | 0 | |a Data protection |x Mathematics. | |
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Differential equations. | |
| 650 | 6 | |a Voies ferrées |x Modèles mathématiques. | |
| 650 | 6 | |a Protection de l'information (Informatique) |x Mathématiques. | |
| 650 | 6 | |a Données volumineuses. | |
| 650 | 6 | |a Équations différentielles. | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Engineering (General) |2 bisacsh | |
| 650 | 7 | |a Big data |2 fast | |
| 650 | 7 | |a Data protection |x Mathematics |2 fast | |
| 650 | 7 | |a Differential equations |2 fast | |
| 650 | 7 | |a Railroad tracks |x Mathematical models |2 fast | |
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