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 Elsevier
Résumé : International audience
Addressing data quality issues in information systems remains a challenging task. Many approaches only tackle this issue at the extract, transform and load steps. Here we define a comprehensive method to gain greater insight into data quality characteristics within data warehouse. Our novel architecture was implemented for an hydroecological case study where massive French watercourse sampling data are collected. The method models and makes effective use of spatial, thematic and temporal accuracy, consistency and completeness for multidimensional data in order to offer analysts a “data quality” oriented framework. The results obtained in experiments carried out on the Saône River dataset demonstrated the relevance of our approach
ISSN: 0098-3004

hal-01223918
https://hal.archives-ouvertes.fr/hal-01223918
https://hal.archives-ouvertes.fr/hal-01223918/document
https://hal.archives-ouvertes.fr/hal-01223918/file/final-cogeo.pdf
DOI : 10.1016/j.cageo.2015.09.012

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

Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
ISSN: 0031-0182

hal-00720714
https://hal.archives-ouvertes.fr/hal-00720714

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 Wiley-VCH Verlag
Résumé : International audience
ISSN: 1439-4235

hal-01234189
https://hal.archives-ouvertes.fr/hal-01234189
DOI : 10.1002/cphc.201500610

Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
ISSN: 0893-9659

hal-01683264
https://hal.univ-antilles.fr/hal-01683264
DOI : 10.1016/S0893-9659(04)90124-3

Éditeur(s) : HAL CCSD Association Internationale de Climatologie
Résumé : 6 pages
International audience
This work presents an overview of spatio-temporal quality andspatial density of rainfall networks of Cameroon, Gabon, Congo, CAP and DRC, particularly in the recentperiod (1973-2014) through the GSOD international database. Faced with enormous gaps observed, theseresults will help to select metrological and analytical orientations in FORGREENE project (Tropical forestsgreeness and cloudiness in Central Africa: present conditions and future evolution). A comparison with rainfallsatellite estimates (RFE type) also indicates their great potential, provided they are based on in situ validations.
Ce travail fait un état des lieux de la qualité temporelle et de la densité spatiale des sériespluviométriques du Cameroun, Gabon, Congo, RCA et RDC, en particulier sur la période récente (1973-2014)grâce à la base de données internationale GSOD. Face aux énormes lacunes observées, ces résultats vontpermettre d’orienter les choix métrologiques et analytiques in situ du programme FORGREENE (Tropicalforests greeness and cloudiness in Central Africa: present conditions and future evolution). Une comparaisonavec des estimations satellitaires (de type RFE) indique aussi leur potentiel intéressant, à condition qu’elless’appuient sur des validations in situ.
Climat et pollution de l'air
Besançon, France

hal-01360178
https://hal.archives-ouvertes.fr/hal-01360178