Database Reference
In-Depth Information
Chapter 8
A Multidimensional Model
for Correct Aggregation of
Geographic Measures
Sandro Bimonte
Cemagref, UR TSCF, France
Marlène Villanova-Oliver
Laboratoire d'Informatique de Grenoble, France
Jerome Gensel
Laboratoire d'Informatique de Grenoble, France
ABStrAct
Spatial OLAP refers to the integration of spatial data in multidimensional applications at physical, logical
and conceptual levels. The multidimensional aggregation of geographic objects (geographic measures)
exhibits theoretical and implementation problems. In this chapter, the authors present a panorama of
aggregation issues in multidimensional, geostatistic, GIS and Spatial OLAP models. Then, they illustrate
how overlapping geometries and dependency of spatial and alphanumeric aggregation are necessary
for correctly aggregating geographic measures. Consequently, they present an extension of the logical
multidimensional model GeoCube (Bimonte et al., 2006) to deal with these issues.
IntroductIon
facts and dimensions . Facts are described by values
called measures . Dimensions, structured in hierar-
chies , permit to analyze facts according to different
analysis axes and at different levels of detail. An
instance of a dimension is a set of members orga-
nized according to the hierarchies. An instance of
the conceptual model is represented by a hypercube
whose axes are the dimension members at the finest
levels. Each cell of a hypercube contains the value
A Data Warehouse (DW) is a centralized reposi-
tory of data acquired from external data sources
and organized following the multidimensional
model (Kimball, 1996) in order to be analyzed by
On-Line Analytical Processing (OLAP) systems.
Multidimensional models rely on the concepts of
Search WWH ::




Custom Search