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A quality-aware spatial data warehouse for querying hydroecological data

Soumia Lilia Berrahou 1 Nathalie Lalande 1 Eva Serrano 2 Guilhem Molla 1 Laure Berti-Équille 2, 3 Sandro Bimonte 4 Sandra Bringay 5 Flavie Cernesson 1 Corinne Grac 6 Dino Ienco 1 Florence Le Ber 7 Maguelonne Teisseire 1
1 UMR TETIS - Territoires, Environnement, Télédétection et Information Spatiale
2 ESPACE-DEV - Département Environnement et Ressources [Montpellier]
3 QCRI - Qatar Computing Research Institute [Doha, Qatar]
4 UR TSCF - Technologies et systèmes d'information pour les agrosystèmes
5 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
6 LIVE - Laboratoire Image, Ville, Environnement [Strasbourg]
7 ICube - Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie
Abstract : 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
Keywords : Hydroecological data Information system Data warehouse modeling and design Data quality
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https://hal.archives-ouvertes.fr/hal-01223918
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Submitted on : Tuesday, November 10, 2015 - 8:18:00 PM
Last modification on : Wednesday, December 1, 2021 - 3:32:11 PM
Long-term archiving on: : Friday, April 28, 2017 - 7:40:14 AM

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CNRS | UNIV-AG | IRSTEA | CIRAD | ADVANSE | AGROPARISTECH | LIRMM | TETIS | MIPS | UNIV-MONTPELLIER | INC-CNRS | SITE-ALSACE | INSA-STRASBOURG | INSA-GROUPE | ENGEES | INRAE | INRAEOCCITANIEMONTPELLIER | AFRIQ | ANR | LRN

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Soumia Lilia Berrahou, Nathalie Lalande, Eva Serrano, Guilhem Molla, Laure Berti-Équille, et al.. A quality-aware spatial data warehouse for querying hydroecological data. Computers & Geosciences, Elsevier, 2015, 85 (part A), pp.126 - 135. ⟨10.1016/j.cageo.2015.09.012⟩. ⟨hal-01223918⟩

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