{"product_id":"torus-1-toward-an-open-resource-using-services-cloud-computing-for-environmental-data-hardback-9781786305992","title":"TORUS 1 - Toward an Open Resource Using Services; Cloud Computing for Environmental Data (Hardback) 9781786305992","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eTORUS 1 - Toward an Open Resource Using Services\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eCloud Computing for Environmental Data\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eDominique Laffly (Edited by), D Laffly (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781786305992, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 13 March 2020\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e352 pages\u003cbr\u003e22.8 x 15.2 x 2.2 cm, 0.612 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThis 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.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1. Integrated Analysis in Geography: The Way to Cloud Computing \u003c\/b\u003e\u003cb\u003exix\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIntroduction to Part 1 \u003c\/b\u003e\u003cb\u003exxi\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDominique LAFFLY\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1. Geographical Information and Landscape, Elements of Formalization \u003c\/b\u003e\u003cb\u003e1\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDominique LAFFLY\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2. Sampling Strategies \u003c\/b\u003e\u003cb\u003e7\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDominique LAFFLY\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1. References 18\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3. Characterization of the Spatial Structure \u003c\/b\u003e\u003cb\u003e19\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDominique LAFFLY\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4. Thematic Information Structures \u003c\/b\u003e\u003cb\u003e27\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDominique LAFFLY\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5. From the Point to the Surface, How to Link Endogenous and Exogenous Data \u003c\/b\u003e\u003cb\u003e35\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDominique LAFFLY\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1. References 44\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6. Big Data in Geography \u003c\/b\u003e\u003cb\u003e45\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDominique LAFFLY\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eConclusion to Part 1 \u003c\/b\u003e\u003cb\u003e55\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDominique LAFFLY\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2. Basic Mathematical, Statistical and Computational Tools \u003c\/b\u003e\u003cb\u003e59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7. An Introduction to Machine Learning \u003c\/b\u003e\u003cb\u003e61\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHichem SAHLI\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1. Predictive modeling: introduction 61\u003c\/p\u003e \u003cp\u003e7.2. Bayesian modeling61\u003c\/p\u003e \u003cp\u003e7.2.1. Basic probability theory 62\u003c\/p\u003e \u003cp\u003e7.2.2. Bayes rule 63\u003c\/p\u003e \u003cp\u003e7.2.3. Parameter estimation 63\u003c\/p\u003e \u003cp\u003e7.2.4. Learning Gaussians 64\u003c\/p\u003e \u003cp\u003e7.3. Generative versus discriminative models 66\u003c\/p\u003e \u003cp\u003e7.4. Classification 67\u003c\/p\u003e \u003cp\u003e7.4.1. Naïve Bayes 68\u003c\/p\u003e \u003cp\u003e7.4.2. Support vector machines 69\u003c\/p\u003e \u003cp\u003e7.5. Evaluation metrics for classification evaluation 71\u003c\/p\u003e \u003cp\u003e7.5.1. Confusion matrix-based measures 71\u003c\/p\u003e \u003cp\u003e7.5.2. Area under the ROC curve (AUC) 73\u003c\/p\u003e \u003cp\u003e7.6. Cross-validation and over-fitting 73\u003c\/p\u003e \u003cp\u003e7.7. References 74\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8. Multivariate Data Analysis \u003c\/b\u003e\u003cb\u003e75\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAstrid JOURDAN and Dominique LAFFLY\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1. Introduction 75\u003c\/p\u003e \u003cp\u003e8.2. Principal component analysis 77\u003c\/p\u003e \u003cp\u003e8.2.1. How to measure the information 78\u003c\/p\u003e \u003cp\u003e8.2.2. Scalar product and orthogonal variables 80\u003c\/p\u003e \u003cp\u003e8.2.3. Construction of the principal axes 81\u003c\/p\u003e \u003cp\u003e8.2.4. Analysis of the principal axes 84\u003c\/p\u003e \u003cp\u003e8.2.5. Analysis of the data points 86\u003c\/p\u003e \u003cp\u003e8.3. Multiple correspondence analysis 88\u003c\/p\u003e \u003cp\u003e8.3.1. Indicator matrix 89\u003c\/p\u003e \u003cp\u003e8.3.2. Cloud of data points 90\u003c\/p\u003e \u003cp\u003e8.3.3. Cloud of levels 92\u003c\/p\u003e \u003cp\u003e8.3.4. MCA or PCA? 94\u003c\/p\u003e \u003cp\u003e8.4. Clustering 96\u003c\/p\u003e \u003cp\u003e8.4.1. Distance between data points 97\u003c\/p\u003e \u003cp\u003e8.4.2. Dissimilarity criteria between clusters 98\u003c\/p\u003e \u003cp\u003e8.4.3. Variance (inertia) decomposition 99\u003c\/p\u003e \u003cp\u003e8.4.4. k-means method 101\u003c\/p\u003e \u003cp\u003e8.4.5. Agglomerative hierarchical clustering 104\u003c\/p\u003e \u003cp\u003e8.5. References 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9. Sensitivity Analysis \u003c\/b\u003e\u003cb\u003e107\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAstrid JOURDAN and Peio LOUBIÈRE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1. Generalities 107\u003c\/p\u003e \u003cp\u003e9.2. Methods based on linear regression 109\u003c\/p\u003e \u003cp\u003e9.2.1. Presentation 109\u003c\/p\u003e \u003cp\u003e9.2.2. R practice 111\u003c\/p\u003e \u003cp\u003e9.3. Morris’ method 114\u003c\/p\u003e \u003cp\u003e9.3.1. Elementary effects method (Morris’ method) 114\u003c\/p\u003e \u003cp\u003e9.3.2. R practice 117\u003c\/p\u003e \u003cp\u003e9.4. Methods based on variance analysis 119\u003c\/p\u003e \u003cp\u003e9.4.1. Sobol’ indices 120\u003c\/p\u003e \u003cp\u003e9.4.2. Estimation of the Sobol’ indices 122\u003c\/p\u003e \u003cp\u003e9.4.3. R practice 123\u003c\/p\u003e \u003cp\u003e9.5. Conclusion 126\u003c\/p\u003e \u003cp\u003e9.6. References 127\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10. Using R for Multivariate Analysis \u003c\/b\u003e\u003cb\u003e129\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAstrid JOURDAN\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1. Introduction 129\u003c\/p\u003e \u003cp\u003e10.1.1. The dataset 131\u003c\/p\u003e \u003cp\u003e10.1.2. The variables 134\u003c\/p\u003e \u003cp\u003e10.2. Principal component analysis 136\u003c\/p\u003e \u003cp\u003e10.2.1. Eigenvalues 137\u003c\/p\u003e \u003cp\u003e10.2.2. Data points (Individuals) 139\u003c\/p\u003e \u003cp\u003e10.2.3. Supplementary variables 143\u003c\/p\u003e \u003cp\u003e10.2.4. Other representations 143\u003c\/p\u003e \u003cp\u003e10.3. Multiple correspondence analysis 144\u003c\/p\u003e \u003cp\u003e10.4. Clustering 145\u003c\/p\u003e \u003cp\u003e10.4.1. k-means algorithm 145\u003c\/p\u003e \u003cp\u003e10.5. References 151\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3. Computer Science \u003c\/b\u003e\u003cb\u003e153\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11. High Performance and Distributed Computing \u003c\/b\u003e\u003cb\u003e155\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSebastiano Fabio SCHIFANO, Eleonora LUPPI, Didin Agustian PERMADI, Thi Kim Oanh NGUYEN, Nhat Ha Chi NGUYEN and Luca TOMASSETTI\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1. High performance computing 155\u003c\/p\u003e \u003cp\u003e11.2. Systems based on multi-core CPUs 157\u003c\/p\u003e \u003cp\u003e11.2.1. Systems based on GPUs 159\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12. Introduction to Distributed Computing \u003c\/b\u003e\u003cb\u003e163\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eEleonora LUPPI\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1. Introduction 163\u003c\/p\u003e \u003cp\u003e12.1.1. A brief history 163\u003c\/p\u003e \u003cp\u003e12.1.2. Design requirements165\u003c\/p\u003e \u003cp\u003e12.1.3. Models 168\u003c\/p\u003e \u003cp\u003e12.1.4. Grid computing 171\u003c\/p\u003e \u003cp\u003e12.2. References 176\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13. Towards Cloud Computing \u003c\/b\u003e\u003cb\u003e179\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePeio LOUBIÈRE and Luca TOMASSETTI\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1. Introduction 179\u003c\/p\u003e \u003cp\u003e13.1.1. Generalities 179\u003c\/p\u003e \u003cp\u003e13.1.2. Benefits and drawbacks 180\u003c\/p\u003e \u003cp\u003e13.2. Service model 180\u003c\/p\u003e \u003cp\u003e13.2.1. Software as a Service 181\u003c\/p\u003e \u003cp\u003e13.2.2. Platform as a Service 182\u003c\/p\u003e \u003cp\u003e13.2.3. Infrastructure as a Service 182\u003c\/p\u003e \u003cp\u003e13.2.4. And many more: XaaS 182\u003c\/p\u003e \u003cp\u003e13.3. Deployment model 183\u003c\/p\u003e \u003cp\u003e13.3.1. Public cloud 183\u003c\/p\u003e \u003cp\u003e13.3.2. Private cloud 183\u003c\/p\u003e \u003cp\u003e13.3.3. Hybrid cloud 184\u003c\/p\u003e \u003cp\u003e13.4. Behind the hood, a technological overview 184\u003c\/p\u003e \u003cp\u003e13.4.1. Structure 184\u003c\/p\u003e \u003cp\u003e13.4.2. Virtualization 185\u003c\/p\u003e \u003cp\u003e13.4.3. Scalability 186\u003c\/p\u003e \u003cp\u003e13.4.4. Web-Oriented Architecture 187\u003c\/p\u003e \u003cp\u003e13.5. Conclusion 187\u003c\/p\u003e \u003cp\u003e13.6. References 188\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14. Web-Oriented Architecture – How to design a RESTFull API \u003c\/b\u003e\u003cb\u003e191\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eFlorent DEVIN\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1. Introduction 191\u003c\/p\u003e \u003cp\u003e14.2. Web services 192\u003c\/p\u003e \u003cp\u003e14.2.1. Introduction 192\u003c\/p\u003e \u003cp\u003e14.2.2. SOAP web services 193\u003c\/p\u003e \u003cp\u003e14.2.3. REST web services 195\u003c\/p\u003e \u003cp\u003e14.3. Web-Oriented Applications – Microservice applications 198\u003c\/p\u003e \u003cp\u003e14.3.1. Stateless and scalabilty 199\u003c\/p\u003e \u003cp\u003e14.3.2. API 200\u003c\/p\u003e \u003cp\u003e14.3.3. HTTP Methods 201\u003c\/p\u003e \u003cp\u003e14.3.4. Example of an API 202\u003c\/p\u003e \u003cp\u003e14.4. WSDL example 203\u003c\/p\u003e \u003cp\u003e14.5. Conclusion 205\u003c\/p\u003e \u003cp\u003e14.6. References 205\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15. SCALA – Functional Programming \u003c\/b\u003e\u003cb\u003e207\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eFlorent DEVIN\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1. Introduction 207\u003c\/p\u003e \u003cp\u003e15.1.1. Programming languages 208\u003c\/p\u003e \u003cp\u003e15.1.2. Paradigm 208\u003c\/p\u003e \u003cp\u003e15.2. Functional programming 212\u003c\/p\u003e \u003cp\u003e15.2.1. Introduction 212\u003c\/p\u003e \u003cp\u003e15.2.2. Why now? 212\u003c\/p\u003e \u003cp\u003e15.2.3. High order function 213\u003c\/p\u003e \u003cp\u003e15.2.4. Basic functional blocks 215\u003c\/p\u003e \u003cp\u003e15.3. Scala 217\u003c\/p\u003e \u003cp\u003e15.3.1. Types systems 218\u003c\/p\u003e \u003cp\u003e15.3.2. Basic manipulation of collection 222\u003c\/p\u003e \u003cp\u003e15.4. Rational 224\u003c\/p\u003e \u003cp\u003e15.5. Why immutability matters? 224\u003c\/p\u003e \u003cp\u003e15.6. Conclusion 226\u003c\/p\u003e \u003cp\u003e15.7. References 227\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16. Spark and Machine Learning Library \u003c\/b\u003e\u003cb\u003e229\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eYannick LE NIR\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1. Introduction 229\u003c\/p\u003e \u003cp\u003e16.2. Spark 230\u003c\/p\u003e \u003cp\u003e16.2.1. Spark introduction 230\u003c\/p\u003e \u003cp\u003e16.2.2. RDD presentation 230\u003c\/p\u003e \u003cp\u003e16.2.3. RDD lifecycle 231\u003c\/p\u003e \u003cp\u003e16.2.4. Operations on RDD 232\u003c\/p\u003e \u003cp\u003e16.2.5. Exercises for environmental sciences 236\u003c\/p\u003e \u003cp\u003e16.3. Spark machine learning library 237\u003c\/p\u003e \u003cp\u003e16.3.1. Local vectors 237\u003c\/p\u003e \u003cp\u003e16.3.2. Labeled points 237\u003c\/p\u003e \u003cp\u003e16.3.3. Learning dataset 238\u003c\/p\u003e \u003cp\u003e16.3.4. Classification and regression algorithms in Spark 238\u003c\/p\u003e \u003cp\u003e16.3.5. Exercises for environmental sciences 239\u003c\/p\u003e \u003cp\u003e16.4. Conclusion 242\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17. Database for Cloud Computing \u003c\/b\u003e\u003cb\u003e245\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePeio LOUBIÈRE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1. Introduction 245\u003c\/p\u003e \u003cp\u003e17.2. From myGlsrdbms to NoSQL 245\u003c\/p\u003e \u003cp\u003e17.2.1. CAP theorem 246\u003c\/p\u003e \u003cp\u003e17.2.2. From ACID to BASE 247\u003c\/p\u003e \u003cp\u003e17.3. NoSQL database storage paradigms 248\u003c\/p\u003e \u003cp\u003e17.3.1. Column-family oriented storage 249\u003c\/p\u003e \u003cp\u003e17.3.2. Key\/value-oriented storage 249\u003c\/p\u003e \u003cp\u003e17.3.3. Document-oriented storage 250\u003c\/p\u003e \u003cp\u003e17.3.4. Graph-oriented storage 251\u003c\/p\u003e \u003cp\u003e17.4. SQL versus NoSQL, the war will not take place 251\u003c\/p\u003e \u003cp\u003e17.5. Example: a dive into MongoDB 252\u003c\/p\u003e \u003cp\u003e17.5.1. Presentation 253\u003c\/p\u003e \u003cp\u003e17.5.2. First steps 254\u003c\/p\u003e \u003cp\u003e17.5.3. Database level commands 254\u003c\/p\u003e \u003cp\u003e17.5.4. Data types 255\u003c\/p\u003e \u003cp\u003e17.5.5. Modifying data 255\u003c\/p\u003e \u003cp\u003e17.6. Conclusion 273\u003c\/p\u003e \u003cp\u003e17.7. References 273\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 18. WRF Performance Analysis and Scalability on Multicore High Performance Computing Systems \u003c\/b\u003e\u003cb\u003e275\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDidin Agustian PERMADI, Sebastiano Fabio SCHIFANO, Thi Kim Oanh NGUYEN, Nhat Ha Chi NGUYEN, Eleonora LUPPI and Luca TOMASSETTI\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1. Introduction 276\u003c\/p\u003e \u003cp\u003e18.2. The weather research and forecast model and experimental set-up 276\u003c\/p\u003e \u003cp\u003e18.2.1. Model architecture 276\u003c\/p\u003e \u003cp\u003e18.3. Architecture of multicore HPC system 282\u003c\/p\u003e \u003cp\u003e18.4. Results 283\u003c\/p\u003e \u003cp\u003e18.4.1. Results of experiment E1 283\u003c\/p\u003e \u003cp\u003e18.4.2. Results of experiment E2 286\u003c\/p\u003e \u003cp\u003e18.5. Conclusion 288\u003c\/p\u003e \u003cp\u003e18.6. References 288\u003c\/p\u003e \u003cp\u003eList of Authors 291\u003c\/p\u003e \u003cp\u003eIndex 293\u003c\/p\u003e \u003cp\u003eSummaries of other volumes 295\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-ISTE","offers":[{"title":"Brand New","offer_id":52446749753624,"sku":"9781786305992","price":100.57,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781786305992.jpg?v=1785112765","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/torus-1-toward-an-open-resource-using-services-cloud-computing-for-environmental-data-hardback-9781786305992","provider":"Freshly Printed Books","version":"1.0","type":"link"}