Title: A hybrid risk-aware design method for spatial datacubes handling spatial vague data: implementation and validation

Authors: Elodie Edoh-Alove; Sandro Bimonte; Yvan Bédard; François Pinet

Addresses: Irstea Clermont-Ferrand Centre, 9 avenue Blaise Pascal CS20085, 63178 Aubière, France ' Irstea Clermont-Ferrand Centre, 9 avenue Blaise Pascal CS20085, 63178 Aubière, France ' Department of Geomatics Sciences, Centre for Research in Geomatics, Laval University, Quebec City, Quebec, Canada ' Irstea Clermont-Ferrand Centre, 9 avenue Blaise Pascal CS20085, 63178 Aubière, France

Abstract: Spatial data warehouses (SDWs) and spatial OLAP (SOLAP) are well-known business intelligence (BI) technologies that aim to support multidimensional and online analysis of huge volumes of data with spatial reference. Spatial vagueness is one of the most neglected imperfections of spatial data. Although several works propose new ad-hoc models for handling spatial vagueness, their implementation in spatial database management systems (DBMS) and SDW is still in an embryonic state. In this paper, we present a new design method for SOLAP datacubes that allows handling vague spatial data analysis issues. This method relies on a risk management method applied to the potential risks of data misinterpretation and decision makers' tolerance levels to those risks. We also present a tool implementing our method and a validation of the method is done based on the designed datacubes schemas testing.

Keywords: spatial vagueness; spatial data warehouse; SDW; spatial OLAP; SOLAP datacubes; risk management; spatial datacubes; risk-aware design; vague data; business intelligence; database management systems; spatial DBMS.

DOI: 10.1504/IJBIDM.2014.068366

International Journal of Business Intelligence and Data Mining, 2014 Vol.9 No.3, pp.210 - 232

Published online: 10 Apr 2015 *

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