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.
Part 1. Integrated Analysis in Geography: The Way
1. Geographical Information and Landscape, Elements of Formalization, Dominique Laffly.
2. Sampling Strategies, Dominique Laffly.
3. Characterization of the Spatial Structure, Dominique Laffly.
4. Thematic Information Structures, Dominique Laffly.
5. From the Point to the Surface, How to Link Endogenous and Exogenous Data, Dominique Laffly.
6. Big Data in Geography, Dominique Laffly.
Part 2. Basic Mathematical, Statistical and Computational Tools
7. An Introduction to Machine Learning, Hichem Sahli.
8. Multivariate Data Analysis, Astrid Jourdan and Dominique Laffly.
9. Sensitivity Analysis, Astrid Jourdan and Peio Loubière.
10. Using R for Multivariate Analysis, Astrid Jourdan.
Part 3. Computer Science
11. High Performance and Distributed Computing, Sebastiano Fabio Schifano, Eleonora Luppi, Didin Agustian Permadi, Thi Kim Oanh Nguyen, Nhat Ha Chi Nguyen and Luca Tomassetti.
12. Introduction to Distributed Computing, Eleonora Luppi.
13. Towards Cloud Computing, Peio Loubière and Luca Tomassetti.
14. Web-Oriented Architecture – How to design a RESTFull API, Florent Devin.
15. SCALA–Functional Programming, Florent Devin.
16. Spark and Machine Learning Library, Yannick Le Nir.
17. Database for Cloud Computing, Peio Loubière.
18. WRF Performance Analysis and Scalability onMulticore High Performance Computing Systems, Didin Agustian Permadi, Sebastiano Fabio Schifano, Thi Kim Oanh Nguyen, Nhat Ha Chi Nguyen, Eleonora Luppi and Luca Tomassetti.
Dominique Lafly is a Professor at the University of Toulouse, France. As a geographer, he is interested in the landscape, and the links between societies and their environment. Concerned with the issue of Big Data, he promotes multidisciplinary programs to bring IT closer to environmental applied disciplines.
Table of Contents
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