Big data and differential privacy: analysis strategies for railway track engineering
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ, USA
John Wiley & Sons, Inc.
2017
|
| Schriftenreihe: | Wiley series in operations research and management science
|
| Schlagworte: | |
| Online-Zugang: | FRO01 UBG01 UBT01 UBY01 Volltext |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | 1 Online-Ressource (xiii, 252 Seiten) Diagramme |
| ISBN: | 9781119229070 9781119229056 9781119229063 |
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| 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, USA |b John Wiley & Sons, Inc. |c 2017 | |
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| 490 | 0 | |a Wiley series in operations research and management science | |
| 500 | |a Includes bibliographical references and index | ||
| 505 | 8 | |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 | |
| 650 | 4 | |a TECHNOLOGY & ENGINEERING / Engineering (General) | |
| 650 | 4 | |a Big data | |
| 650 | 4 | |a Data protection / Mathematics | |
| 650 | 4 | |a Differential equations | |
| 650 | 4 | |a Railroad tracks / Mathematical models | |
| 650 | 4 | |a Mathematik | |
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Railroad tracks / Mathematical models | |
| 650 | 4 | |a Data protection / Mathematics | |
| 650 | 4 | |a Big data | |
| 650 | 4 | |a Differential equations | |
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Datensatz im Suchindex
| _version_ | 1804177893128929280 |
|---|---|
| any_adam_object | |
| author | Attoh-Okine, Nii O. |
| author_facet | Attoh-Okine, Nii O. |
| author_role | aut |
| author_sort | Attoh-Okine, Nii O. |
| author_variant | n o a o noa noao |
| building | Verbundindex |
| bvnumber | BV044537192 |
| callnumber-first | T - Technology |
| callnumber-label | TF241 |
| callnumber-raw | TF241 |
| callnumber-search | TF241 |
| callnumber-sort | TF 3241 |
| callnumber-subject | TF - Railroad Engineering and Operation |
| classification_rvk | QH 234 |
| collection | ZDB-35-WIC |
| contents | 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 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 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) 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 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 |
| ctrlnum | (OCoLC)1011368969 (DE-599)BVBBV044537192 |
| dewey-full | 625.1/4028557 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 625 - Engineering of railroads and roads |
| dewey-raw | 625.1/4028557 |
| dewey-search | 625.1/4028557 |
| dewey-sort | 3625.1 74028557 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Wirtschaftswissenschaften Verkehr / Transport |
| format | Electronic eBook |
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| id | DE-604.BV044537192 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:55:18Z |
| institution | BVB |
| isbn | 9781119229070 9781119229056 9781119229063 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029936334 |
| oclc_num | 1011368969 |
| open_access_boolean | |
| owner | DE-706 DE-861 DE-703 |
| owner_facet | DE-706 DE-861 DE-703 |
| physical | 1 Online-Ressource (xiii, 252 Seiten) Diagramme |
| psigel | ZDB-35-WIC UBG_PDA_WIC ZDB-35-WIC FRO_PDA_WIC ZDB-35-WIC UBG_PDA_WIC ZDB-35-WIC UBT_PDA_WIC_Kauf ZDB-35-WIC UBY_PDA_WIC_Kauf |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | John Wiley & Sons, Inc. |
| record_format | marc |
| series2 | Wiley series in operations research and management science |
| spelling | Attoh-Okine, Nii O. Verfasser aut Big data and differential privacy analysis strategies for railway track engineering Nii O. Attoh-Okine Hoboken, NJ, USA John Wiley & Sons, Inc. 2017 1 Online-Ressource (xiii, 252 Seiten) Diagramme txt rdacontent c rdamedia cr rdacarrier Wiley series in operations research and management science Includes bibliographical references and index 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 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 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) 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 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 TECHNOLOGY & ENGINEERING / Engineering (General) Big data Data protection / Mathematics Differential equations Railroad tracks / Mathematical models Mathematik Mathematisches Modell Datenanalyse (DE-588)4123037-1 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Eisenbahntechnik (DE-588)4151498-1 gnd rswk-swf Big Data (DE-588)4802620-7 gnd rswk-swf Big Data (DE-588)4802620-7 s Eisenbahntechnik (DE-588)4151498-1 s Datenanalyse (DE-588)4123037-1 s Mathematisches Modell (DE-588)4114528-8 s DE-604 Erscheint auch als Druck-Ausgabe 978-1-119-22904-9 https://onlinelibrary.wiley.com/doi/book/10.1002/9781119229070 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Attoh-Okine, Nii O. Big data and differential privacy analysis strategies for railway track engineering 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 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 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) 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 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 TECHNOLOGY & ENGINEERING / Engineering (General) Big data Data protection / Mathematics Differential equations Railroad tracks / Mathematical models Mathematik Mathematisches Modell Datenanalyse (DE-588)4123037-1 gnd Mathematisches Modell (DE-588)4114528-8 gnd Eisenbahntechnik (DE-588)4151498-1 gnd Big Data (DE-588)4802620-7 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4114528-8 (DE-588)4151498-1 (DE-588)4802620-7 |
| title | Big data and differential privacy analysis strategies for railway track engineering |
| title_auth | Big data and differential privacy analysis strategies for railway track engineering |
| title_exact_search | Big data and differential privacy analysis strategies for railway track engineering |
| title_full | Big data and differential privacy analysis strategies for railway track engineering Nii O. Attoh-Okine |
| title_fullStr | Big data and differential privacy analysis strategies for railway track engineering Nii O. Attoh-Okine |
| title_full_unstemmed | Big data and differential privacy analysis strategies for railway track engineering Nii O. Attoh-Okine |
| title_short | Big data and differential privacy |
| title_sort | big data and differential privacy analysis strategies for railway track engineering |
| title_sub | analysis strategies for railway track engineering |
| topic | TECHNOLOGY & ENGINEERING / Engineering (General) Big data Data protection / Mathematics Differential equations Railroad tracks / Mathematical models Mathematik Mathematisches Modell Datenanalyse (DE-588)4123037-1 gnd Mathematisches Modell (DE-588)4114528-8 gnd Eisenbahntechnik (DE-588)4151498-1 gnd Big Data (DE-588)4802620-7 gnd |
| topic_facet | TECHNOLOGY & ENGINEERING / Engineering (General) Big data Data protection / Mathematics Differential equations Railroad tracks / Mathematical models Mathematik Mathematisches Modell Datenanalyse Eisenbahntechnik Big Data |
| url | https://onlinelibrary.wiley.com/doi/book/10.1002/9781119229070 |
| work_keys_str_mv | AT attohokineniio bigdataanddifferentialprivacyanalysisstrategiesforrailwaytrackengineering |