| 000 -LEADER |
|---|
| fixed length control field |
06496nam a2200349 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20240506112234.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
230329b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781119229070 |
| 040 ## - CATALOGING SOURCE |
|---|
| Language of cataloging |
eng |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
eng |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
TF241 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
625.14028557 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Preferred name for the person |
Attoh-Okine, Nii O. |
| Relator term |
author |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Big data and differential privacy : |
| Remainder of title |
analysis strategies for railway track engineering / |
| Statement of responsibility, etc |
Nii O. Attoh-Okine. |
| 264 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
New Jersey : |
| Name of publisher, distributor, etc |
John Wiley & Sons Inc., |
| Date of publication, distribution, etc |
2017. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
1 online resource. |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Content type code |
txt |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
computer |
| Media type code |
c |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
online resource |
| Carrier type code |
cr |
| Source |
rdacarrier |
| 490 0# - SERIES STATEMENT |
|---|
| Series statement |
Wiley series in operations research and management science |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 505 0# - CONTENTS |
|---|
| Formatted contents note |
TABLE OF CONTENTS<br/>Preface xi<br/><br/>Acknowledgments xiii<br/><br/>1 Introduction 1<br/><br/>1.1 General 1<br/><br/>1.2 Track Components 2<br/><br/>1.3 Characteristics of Railway Track Data 4<br/><br/>1.4 Railway Track Engineering Problems 6<br/><br/>1.5 Wheel–Rail Interface Data 11<br/><br/>1.6 Geometry Data 15<br/><br/>1.7 Track Geometry DegradationModels 20<br/><br/>1.8 Rail Defect Data 25<br/><br/>1.9 Inspection and Detection Systems 33<br/><br/>1.10 Rail Grinding 37<br/><br/>1.11 Traditional Data Analysis Techniques 40<br/><br/>1.12 Remarks 41<br/><br/>References 42<br/><br/>2 Data Analysis – Basic Overview 49<br/><br/>2.1 Introduction 49<br/><br/>2.2 Exploratory Data Analysis (EDA) 49<br/><br/>2.3 Symbolic Data Analysis 53<br/><br/>2.4 Imputation 54<br/><br/>2.5 Bayesian Methods and Big Data Analysis 56<br/><br/>2.6 Remarks 57<br/><br/>References 57<br/><br/>3 Machine Learning: A Basic Overview 59<br/><br/>3.1 Introduction 59<br/><br/>3.2 Supervised Learning 60<br/><br/>3.3 Unsupervised Learning 61<br/><br/>3.4 Semi-Supervised Learning 61<br/><br/>3.5 Reinforcement Learning 61<br/><br/>3.6 Data Integration 63<br/><br/>3.7 Data Science Ontology 63<br/><br/>3.8 Imbalanced Classification 69<br/><br/>3.9 Model Validation 70<br/><br/>3.10 Ensemble Methods 71<br/><br/>3.11 Big P and Small N (P â N) 74<br/><br/>3.12 Deep Learning 79<br/><br/>3.13 Data Stream Processing 95<br/><br/>3.14 Remarks 105<br/><br/>References 105<br/><br/>4 Basic Foundations of Big Data 113<br/><br/>4.1 Introduction 113<br/><br/>4.2 Query 116<br/><br/>4.3 Taxonomy of Big Data Analytics in Railway Track Engineering 123<br/><br/>4.4 Data Engineering 124<br/><br/>4.5 Remarks 130<br/><br/>References 130<br/><br/>5 Hilbert–Huang Transform, Profile, Signal, and Image Analysis 133<br/><br/>5.1 Hilbert–Huang Transform 133<br/><br/>5.2 Axle Box Acceleration 150<br/><br/>5.3 Analysis 151<br/><br/>5.4 Remarks 153<br/><br/>References 153<br/><br/>6 Tensors – Big Data in Multidimensional Settings 157<br/><br/>6.1 Introduction 157<br/><br/>6.2 Notations and Definitions 158<br/><br/>6.3 Tensor Decomposition Models 161<br/><br/>6.4 Application 164<br/><br/>6.5 Remarks 170<br/><br/>References 171<br/><br/>7 Copula Models 175<br/><br/>7.1 Introduction 175<br/><br/>7.2 Pair Copula: Vines 184<br/><br/>7.3 Computational Example 186<br/><br/>7.4 Remarks 192<br/><br/>References 193<br/><br/>8 Topological Data Analysis 197<br/><br/>8.1 Introduction 197<br/><br/>8.2 Basic Ideas 197<br/><br/>8.3 A Simple Railway Track Engineering Application 203<br/><br/>8.4 Remarks 204<br/><br/>References 204<br/><br/>9 Bayesian Analysis 207<br/><br/>9.1 Introduction 207<br/><br/>9.2 Markov Chain Monte Carlo (MCMC) 210<br/><br/>9.3 Approximate Bayesian Computation 210<br/><br/>9.4 Markov Chain Monte Carlo Application 216<br/><br/>9.5 ABC Application 219<br/><br/>9.6 Remarks 221<br/><br/>References 222<br/><br/>10 Basic Bayesian Nonparametrics 225<br/><br/>10.1 General 225<br/><br/>10.2 Dirichlet Family 226<br/><br/>10.3 Dirichlet Process 227<br/><br/>10.4 Finite Mixture Modeling 231<br/><br/>10.5 Bayesian Nonparametric Railway Track 232<br/><br/>10.6 Remarks 233<br/><br/>References 233<br/><br/>11 Basic Metaheuristics 235<br/><br/>11.1 Introduction 235<br/><br/>11.2 Remarks 237<br/><br/>References 239<br/><br/>12 Differential Privacy 241<br/><br/>12.1 General 241<br/><br/>12.2 Differential Privacy 242<br/><br/>12.3 Remarks 247<br/><br/>References 247<br/><br/>Index 249 |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
A comprehensive introduction to the theory and practice of contemporary data science analysis for railway track engineering<br/><br/>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.<br/><br/>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.<br/><br/>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:<br/><br/>• Features a unified framework for handling large volumes of data in railway track engineering using predictive analytics, machine learning, and data mining<br/><br/>• Explores issues of big data and differential privacy and discusses the various advantages and disadvantages of more conventional data analysis techniques<br/><br/>• Implements big data applications while addressing common issues in railway track maintenance<br/><br/>• 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<br/><br/>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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Railroad tracks |
| General subdivision |
Mathematical models. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data protection |
| General subdivision |
Mathematics. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Big data. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Differential equations. |
| 655 #4 - INDEX TERM--GENRE/FORM |
|---|
| Genre/form data or focus term |
Electronic books. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Wiley series in operations research and management science. |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| Link text |
Full text available at Wiley Online Library Click here to view |
| Uniform Resource Identifier |
https://onlinelibrary.wiley.com/doi/book/10.1002/9781119229070 |
| 942 ## - ADDED ENTRY ELEMENTS |
|---|
| Source of classification or shelving scheme |
|
| Item type |
EBOOK |