New Design Approach to Handle Spatial Vagueness in Spatial OLAP Datacubes: Application to Agri-environmental Data

Elodie Edoh-Alove, Sandro Bimonte, François Pinet and Yvan Bédard
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Elodie Edoh-Alove: Irstea Centre de Clermont-Ferrand, Aubière, France & Centre de Recherche en Géomatique et Département des Sciences Géomatique, Université Laval, Quebec, Canada
Sandro Bimonte: Irstea Centre de Clermont-Ferrand, Aubière, France
François Pinet: Irstea Centre de Clermont-Ferrand, Aubière, France
Yvan Bédard: Centre de Recherche en Géomatique et Département des Sciences Géomatique, Université Laval, Quebec, Canada

International Journal of Agricultural and Environmental Information Systems (IJAEIS), 2015, vol. 6, issue 3, 29-49

Abstract: Spatial-OLAP (SOLAP) technologies are dedicated to multidimensional analysis of large volumes of (spatial) data. Spatial data are subject to different types of uncertainty, in particular spatial vagueness. Although several researches propose new models to cope with spatial vagueness, their integration in SOLAP systems is still in an embryonic state. Also, analyzing multidimensional data with metadata brought by the exploitation of the new models can be too complex and demanding for decision-makers. To help reduce spatial vagueness consequences on the exactness of SOLAP analysis queries, the authors present a new approach for designing SOLAP datacubes based on end-users' tolerance to the risks of misinterpretation of fact data. An experimentation of the new approach on agri-environmental data is also proposed.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jaeis0:v:6:y:2015:i:3:p:29-49

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