Éditeur(s) : HAL CCSD
Résumé : International audience
It is now well recognized that information management systems often deal with geographic information even if they often do not take advantage of it. In these systems, requirements engineering (RE) is becoming more and more important in helping stakeholders to model their requirements regarding the quality of the data.However, to date, few RE projects take geographic information and geographic constraints into account. We thus propose to study in this paper the question of managing such geographic constraints in the framework of RE. We propose an extension of the RE model and its implementation within the KAOS framework, and the use of geographic ontologies
2nd International Conference on Knowledge Management, Information and Knowledge Systems
Hammamet, Tunisia

lirmm-01230140
https://hal-lirmm.ccsd.cnrs.fr/lirmm-01230140

Éditeur(s) : HAL CCSD Public Library of Science
Résumé : International audience
Understanding the factors underlying the spatio-temporal distribution of infectious diseases provides useful information regarding their prevention and control. Dengue fever spatio-temporal patterns result from complex interactions between the virus, the host, and the vector. These interactions can be influenced by environmental conditions. Our objectives were to analyse dengue fever spatial distribution over New Caledonia during epidemic years, to identify some of the main underlying factors, and to predict the spatial evolution of dengue fever under changing climatic conditions, at the 2100 horizon. We used principal component analysis and support vector machines to analyse and model the influence of climate and socio-economic variables on the mean spatial distribution of 24,272 dengue cases reported from 1995 to 2012 in thirty-three communes of New Caledonia. We then modelled and estimated the future evolution of dengue incidence rates using a regional downscaling of future climate projections. The spatial distribution of dengue fever cases is highly heterogeneous. The variables most associated with this observed heterogeneity are the mean temperature, the mean number of people per premise, and the mean percentage of unemployed people, a variable highly correlated with people's way of life. Rainfall does not seem to play an important role in the spatial distribution of dengue cases during epidemics. By the end of the 21st century, if temperature increases by approximately 3°C, mean incidence rates during epidemics could double. In New Caledonia, a subtropical insular environment, both temperature and socio-economic conditions are influencing the spatial spread of dengue fever. Extension of this study to other countries worldwide should improve the knowledge about climate influence on dengue burden and about the complex interplay between different factors. This study presents a methodology that can be used as a step by step guide to model dengue spatial heterogeneity in other countries.
ISSN: 1935-2727

Droits : http://creativecommons.org/licenses/by/
pasteur-01247037
https://hal-riip.archives-ouvertes.fr/pasteur-01247037
https://hal-riip.archives-ouvertes.fr/pasteur-01247037/document
https://hal-riip.archives-ouvertes.fr/pasteur-01247037/file/journal.pntd.0004211.pdf
DOI : 10.1371/journal.pntd.0004211

Éditeur(s) : HAL CCSD
Résumé : Séminaire de Synthèse du Projet FPPSM "Forêts, Parcs, Pauvreté au Sud de Madagascar"
A Madagascar, la déforestation mobilise toutes les recherches, mais il manque encore une base de données complète, référencée et réactualisable sur les dynamiques forestières. Une solution est la valorisation d'images satellitaires multidates qui, de ce fait, sont issues de capteurs différents en termes de résolution spatiale, radiométrique et temporelle (Spot, Landsat, Modis). Ces données géolocalisées sont volumineuses, elles doivent être normalisées puis stockées et finalement traitées. Face à l'hétérogénéité des données et traitements, l'interopérabilité des ressources est assurée par le standard ISO (International Standard Organisation) et la spécification OGC (Open Geospatial Consortium). Le traitement de séries chronologiques de la couverture forestière exige un outil réutilisable qui s'adapte à de nouvelles données satellitaires : des chaînes de traitement pour les données simples, des workflows pour les gros volumes de données. La modélisation du workflow est illustrée par la construction de sa base de connaissance en utilisant un contexte de travail.
https://hal.archives-ouvertes.fr/hal-01370017

hal-01370017
https://hal.archives-ouvertes.fr/hal-01370017
IRD : fdi:010066424

Éditeur(s) : HAL CCSD
Résumé : International audience
Data integration and knowledge capitalization combine data and information coming from different data sources designed by different experts having different purposes. In this paper, we propose to assist the underlying model merging activity. For close models made by experts of various specialities on the same system, we partially automate the identification of a Greatest Common Model (GCM) which is composed of the common concepts (core-concepts) of the different models. Our methodology is based on Formal Concept Analysis which is a method of data analysis based on lattice theory. A decision tree allows to semiautomatically classify concepts from the concept lattices and assist the GCM extraction. We apply our approach on the EIS-Pesticide project, an environmental information system which aims at centralizing knowledge and information produced by different research teams.
14th International Conference on Enterprise Information Systems (ICEIS 2012)
Wroclaw, Poland

lirmm-01319858
http://hal-lirmm.ccsd.cnrs.fr/lirmm-01319858
DOI : 10.1007/978-3-642-40654-6_5

Éditeur(s) : HAL CCSD
Résumé : LPSC14037
Scientific data collected with modern sensors or dedicated detectors exceed very often the perimeter of the initial scientific design. These data are obtained more and more frequently with large material and human efforts. A large class of scientific experiments are in fact unique because of their large scale, with very small chances to be repeated and to superseded by new experiments in the same domain: for instance high energy physics and astrophysics experiments involve multi-annual developments and a simple duplication of efforts in order to reproduce old data is simply not affordable. Other scientific experiments are in fact unique by nature: earth science, medical sciences etc. since the collected data is "time-stamped" and thereby non-reproducible by new experiments or observations. In addition, scientific data collection increased dramatically in the recent years, participating to the so-called "data deluge" and inviting for common reflection in the context of "big data" investigations. The new knowledge obtained using these data should be preserved long term such that the access and the re-use are made possible and lead to an enhancement of the initial investment. Data observatories, based on open access policies and coupled with multi-disciplinary techniques for indexing and mining may lead to truly new paradigms in science. It is therefore of outmost importance to pursue a coherent and vigorous approach to preserve the scientific data at long term. The preservation remains nevertheless a challenge due to the complexity of the data structure, the fragility of the custom-made software environments as well as the lack of rigorous approaches in workflows and algorithms. To address this challenge, the PREDON project has been initiated in France in 2012 within the MASTODONS program: a Big Data scientific challenge, initiated and supported by the Interdisciplinary Mission of the National Centre for Scientific Research (CNRS). PREDON is a study group formed by researchers from different disciplines and institutes. Several meetings and workshops lead to a rich exchange in ideas, paradigms and methods. The present document includes contributions of the participants to the PREDON Study Group, as well as invited papers, related to the scientific case, methodology and technology. This document should be read as a "facts finding" resource pointing to a concrete and significant scientific interest for long term research data preservation, as well as to cutting edge methods and technologies to achieve this goal. A sustained, coherent and long term action in the area of scientific data preservation would be highly beneficial.
http://hal.in2p3.fr/in2p3-00959072

in2p3-00959072
http://hal.in2p3.fr/in2p3-00959072
http://hal.in2p3.fr/in2p3-00959072/document
http://hal.in2p3.fr/in2p3-00959072/file/PREDON_Scientific_Data_Preservation_2014.pdf

Éditeur(s) : HAL CCSD SciTePress
Résumé : International audience
Data integration and knowledge capitalization combine data and information coming from different data sources designed by different experts having different purposes. In this paper, we propose to assist the underlying model merging activity. For close models made by experts of various specialities, we partially automate the identification of a Greatest Common Model (GCM) which is composed of the common concepts (core concepts) of the different models. Our methodology is based on Formal Concept Analysis which is a method of data analysis based on lattice theory. A decision tree allows to semi-automatically classify concepts from the concept lattices and assist the GCM extraction. We apply our approach on the EIS-Pesticide project, an environmental information system which aims at centralizing knowledge and information produced by different specialized teams.
ICEIS 2012: 14th International Conference on Enterprise Information Systems
Wroclaw, Poland

lirmm-00727009
https://hal-lirmm.ccsd.cnrs.fr/lirmm-00727009