A quality-aware spatial data warehouse for querying hydroecological data

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
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Computers and Geosciences, Elsevier, 2015, 85, pp.126 - 135. 〈10.1016/j.cageo.2015.09.012〉
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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 and Geosciences, Elsevier, 2015, 85, pp.126 - 135. 〈10.1016/j.cageo.2015.09.012〉. 〈hal-01223918〉

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