图书简介
This book, presented in three volumes, examines "environmental" 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.
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. Naive 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 LOUBIERE 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 LOUBIERE 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 LOUBIERE 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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