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TORUS 1 - Toward an Open Resource Using Services. Cloud Computing for Environmental Data. Edition No. 1

  • ID: 5186477
  • Book
  • March 2020
  • 340 Pages
  • John Wiley and Sons Ltd
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This book, presented in three volumes, examines Âenvironmental disciplines in relation to major players in contemporary science: Big Data, artificial intelligence and cloud computing. Today, there is a real sense of urgency regarding the evolution of computer technology, the ever-increasing volume of data, threats to our climate and the sustainable development of our planet. As such, we need to reduce technology just as much as we need to bridge the global socio-economic gap between the North and South; between universal free access to data (open data) and free software (open source). In this book, we pay particular attention to certain environmental subjects, in order to enrich our understanding of cloud computing. These subjects are: erosion; urban air pollution and atmospheric pollution in Southeast Asia; melting permafrost (causing the accelerated release of soil organic carbon in the atmosphere); alert systems of environmental hazards (such as forest fires, prospective modeling of socio-spatial practices and land use); and web fountains of geographical data. Finally, this book asks the question: in order to find a pattern in the data, how do we move from a traditional computing model-based world to pure mathematical research? After thorough examination of this topic, we conclude that this goal is both transdisciplinary and achievable.
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Preface xiii

Part 1. Integrated Analysis in Geography: The Way to Cloud Computing xix

Introduction to Part 1 xxi
Dominique LAFFLY

Chapter 1. Geographical Information and Landscape, Elements of Formalization 1
Dominique LAFFLY

Chapter 2. Sampling Strategies 7
Dominique LAFFLY

2.1. References 18

Chapter 3. Characterization of the Spatial Structure 19
Dominique LAFFLY

Chapter 4. Thematic Information Structures 27
Dominique LAFFLY

Chapter 5. From the Point to the Surface, How to Link Endogenous and Exogenous Data 35
Dominique LAFFLY

5.1. References 44

Chapter 6. Big Data in Geography 45
Dominique LAFFLY

Conclusion to Part 1 55
Dominique LAFFLY

Part 2. Basic Mathematical, Statistical and Computational Tools 59

Chapter 7. An Introduction to Machine Learning 61
Hichem SAHLI

7.1. Predictive modeling: introduction 61

7.2. Bayesian modeling61

7.2.1. Basic probability theory 62

7.2.2. Bayes rule 63

7.2.3. Parameter estimation 63

7.2.4. Learning Gaussians 64

7.3. Generative versus discriminative models 66

7.4. Classification 67

7.4.1. Naïve Bayes 68

7.4.2. Support vector machines 69

7.5. Evaluation metrics for classification evaluation 71

7.5.1. Confusion matrix-based measures 71

7.5.2. Area under the ROC curve (AUC) 73

7.6. Cross-validation and over-fitting 73

7.7. References 74

Chapter 8. Multivariate Data Analysis 75
Astrid JOURDAN and Dominique LAFFLY

8.1. Introduction 75

8.2. Principal component analysis 77

8.2.1. How to measure the information 78

8.2.2. Scalar product and orthogonal variables 80

8.2.3. Construction of the principal axes 81

8.2.4. Analysis of the principal axes 84

8.2.5. Analysis of the data points 86

8.3. Multiple correspondence analysis 88

8.3.1. Indicator matrix 89

8.3.2. Cloud of data points 90

8.3.3. Cloud of levels 92

8.3.4. MCA or PCA? 94

8.4. Clustering 96

8.4.1. Distance between data points 97

8.4.2. Dissimilarity criteria between clusters 98

8.4.3. Variance (inertia) decomposition 99

8.4.4. k-means method 101

8.4.5. Agglomerative hierarchical clustering 104

8.5. References 105

Chapter 9. Sensitivity Analysis 107
Astrid JOURDAN and Peio LOUBIÈRE

9.1. Generalities 107

9.2. Methods based on linear regression 109

9.2.1. Presentation 109

9.2.2. R practice 111

9.3. Morris’ method 114

9.3.1. Elementary effects method (Morris’ method) 114

9.3.2. R practice 117

9.4. Methods based on variance analysis 119

9.4.1. Sobol’ indices 120

9.4.2. Estimation of the Sobol’ indices 122

9.4.3. R practice 123

9.5. Conclusion 126

9.6. References 127

Chapter 10. Using R for Multivariate Analysis 129
Astrid JOURDAN

10.1. Introduction 129

10.1.1. The dataset 131

10.1.2. The variables 134

10.2. Principal component analysis 136

10.2.1. Eigenvalues 137

10.2.2. Data points (Individuals) 139

10.2.3. Supplementary variables 143

10.2.4. Other representations 143

10.3. Multiple correspondence analysis 144

10.4. Clustering 145

10.4.1. k-means algorithm 145

10.5. References 151

Part 3. Computer Science 153

Chapter 11. High Performance and Distributed Computing 155
Sebastiano Fabio SCHIFANO, Eleonora LUPPI, Didin Agustian PERMADI, Thi Kim Oanh NGUYEN, Nhat Ha Chi NGUYEN and Luca TOMASSETTI

11.1. High performance computing 155

11.2. Systems based on multi-core CPUs 157

11.2.1. Systems based on GPUs 159

Chapter 12. Introduction to Distributed Computing 163
Eleonora LUPPI

12.1. Introduction 163

12.1.1. A brief history 163

12.1.2. Design requirements165

12.1.3. Models 168

12.1.4. Grid computing 171

12.2. References 176

Chapter 13. Towards Cloud Computing 179
Peio LOUBIÈRE and Luca TOMASSETTI

13.1. Introduction 179

13.1.1. Generalities 179

13.1.2. Benefits and drawbacks 180

13.2. Service model 180

13.2.1. Software as a Service 181

13.2.2. Platform as a Service 182

13.2.3. Infrastructure as a Service 182

13.2.4. And many more: XaaS 182

13.3. Deployment model 183

13.3.1. Public cloud 183

13.3.2. Private cloud 183

13.3.3. Hybrid cloud 184

13.4. Behind the hood, a technological overview 184

13.4.1. Structure 184

13.4.2. Virtualization 185

13.4.3. Scalability 186

13.4.4. Web-Oriented Architecture 187

13.5. Conclusion 187

13.6. References 188

Chapter 14. Web-Oriented Architecture – How to design a RESTFull API 191
Florent DEVIN

14.1. Introduction 191

14.2. Web services 192

14.2.1. Introduction 192

14.2.2. SOAP web services 193

14.2.3. REST web services 195

14.3. Web-Oriented Applications – Microservice applications 198

14.3.1. Stateless and scalabilty 199

14.3.2. API 200

14.3.3. HTTP Methods 201

14.3.4. Example of an API 202

14.4. WSDL example 203

14.5. Conclusion 205

14.6. References 205

Chapter 15. SCALA – Functional Programming 207
Florent DEVIN

15.1. Introduction 207

15.1.1. Programming languages 208

15.1.2. Paradigm 208

15.2. Functional programming 212

15.2.1. Introduction 212

15.2.2. Why now? 212

15.2.3. High order function 213

15.2.4. Basic functional blocks 215

15.3. Scala 217

15.3.1. Types systems 218

15.3.2. Basic manipulation of collection 222

15.4. Rational 224

15.5. Why immutability matters? 224

15.6. Conclusion 226

15.7. References 227

Chapter 16. Spark and Machine Learning Library 229
Yannick LE NIR

16.1. Introduction 229

16.2. Spark 230

16.2.1. Spark introduction 230

16.2.2. RDD presentation 230

16.2.3. RDD lifecycle 231

16.2.4. Operations on RDD 232

16.2.5. Exercises for environmental sciences 236

16.3. Spark machine learning library 237

16.3.1. Local vectors 237

16.3.2. Labeled points 237

16.3.3. Learning dataset 238

16.3.4. Classification and regression algorithms in Spark 238

16.3.5. Exercises for environmental sciences 239

16.4. Conclusion 242

Chapter 17. Database for Cloud Computing 245
Peio LOUBIÈRE

17.1. Introduction 245

17.2. From myGlsrdbms to NoSQL 245

17.2.1. CAP theorem 246

17.2.2. From ACID to BASE 247

17.3. NoSQL database storage paradigms 248

17.3.1. Column-family oriented storage 249

17.3.2. Key/value-oriented storage 249

17.3.3. Document-oriented storage 250

17.3.4. Graph-oriented storage 251

17.4. SQL versus NoSQL, the war will not take place 251

17.5. Example: a dive into MongoDB 252

17.5.1. Presentation 253

17.5.2. First steps 254

17.5.3. Database level commands 254

17.5.4. Data types 255

17.5.5. Modifying data 255

17.6. Conclusion 273

17.7. References 273

Chapter 18. WRF Performance Analysis and Scalability on Multicore High Performance Computing Systems 275
Didin Agustian PERMADI, Sebastiano Fabio SCHIFANO, Thi Kim Oanh NGUYEN, Nhat Ha Chi NGUYEN, Eleonora LUPPI and Luca TOMASSETTI

18.1. Introduction 276

18.2. The weather research and forecast model and experimental set-up 276

18.2.1. Model architecture 276

18.3. Architecture of multicore HPC system 282

18.4. Results 283

18.4.1. Results of experiment E1 283

18.4.2. Results of experiment E2 286

18.5. Conclusion 288

18.6. References 288

List of Authors 291

Index 293

Summaries of other volumes 295

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Dominique Laffly
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