Big Data and Differential Privacy : (Record no. 74421)

MARC details
000 -LEADER
fixed length control field 07245nam a22004693i 4500
001 - CONTROL NUMBER
control field EBC4860513
003 - CONTROL NUMBER IDENTIFIER
control field MiAaPQ
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20191126093041.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
fixed length control field m o d |
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr cnu||||||||
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 191125s2017 xx o ||||0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781119229056
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 9781119229049
035 ## - SYSTEM CONTROL NUMBER
System control number (MiAaPQ)EBC4860513
035 ## - SYSTEM CONTROL NUMBER
System control number (Au-PeEL)EBL4860513
035 ## - SYSTEM CONTROL NUMBER
System control number (CaPaEBR)ebr11385347
035 ## - SYSTEM CONTROL NUMBER
System control number (CaONFJC)MIL1011171
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)974487431
040 ## - CATALOGING SOURCE
Original cataloging agency MiAaPQ
Language of cataloging eng
Description conventions rda
-- pn
Transcribing agency MiAaPQ
Modifying agency MiAaPQ
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TF241.A886 2017
082 0# - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 625.14028557
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Attoh-Okine, Nii.
9 (RLIN) 44287
245 10 - TITLE STATEMENT
Title Big Data and Differential Privacy :
Remainder of title Analysis Strategies for Railway Track Engineering.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture New York :
Name of producer, publisher, distributor, manufacturer John Wiley & Sons, Incorporated,
Date of production, publication, distribution, manufacture, or copyright notice 2017.
264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Date of production, publication, distribution, manufacture, or copyright notice ©2017.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource (270 pages)
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 1# - SERIES STATEMENT
Series statement Wiley Series in Operations Research and Management Science Ser.
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 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.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 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.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 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.
588 ## - SOURCE OF DESCRIPTION NOTE
Source of description note Description based on publisher supplied metadata and other sources.
590 ## - LOCAL NOTE (RLIN)
Local note Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Railroad tracks--Mathematical models.
9 (RLIN) 44288
655 #4 - INDEX TERM--GENRE/FORM
Genre/form data or focus term Electronic books.
9 (RLIN) 44289
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Print version:
Main entry heading Attoh-Okine, Nii
Title Big Data and Differential Privacy : Analysis Strategies for Railway Track Engineering
Place, publisher, and date of publication New York : John Wiley & Sons, Incorporated,c2017
International Standard Book Number 9781119229049
797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN)
Corporate name or jurisdiction name as entry element ProQuest (Firm)
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Wiley Series in Operations Research and Management Science Ser.
9 (RLIN) 44290
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://ebookcentral.proquest.com/lib/thebc/detail.action?docID=4860513">https://ebookcentral.proquest.com/lib/thebc/detail.action?docID=4860513</a>
Public note Click to View
887 ## - NON-MARC INFORMATION FIELD
Content of non-MARC field EBK
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Ebrary
Holdings
Withdrawn status Lost status Damaged status Not for loan Home library Current library Date acquired Total Checkouts Barcode Date last seen Price effective from Koha item type
        Afghanistan Afghanistan 26/11/2019   EBKAF-N00070875 26/11/2019 26/11/2019 Ebrary
        Algeria Algeria           Ebrary
        Cyprus Cyprus           Ebrary
        Egypt Egypt           Ebrary
        Libya Libya           Ebrary
        Morocco Morocco           Ebrary
        Nepal Nepal 26/11/2019   EBKNP-N00070875 26/11/2019 26/11/2019 Ebrary
        Sudan Sudan           Ebrary
        Tunisia Tunisia           Ebrary