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Bédard et al. (2005), these solutions provide all
GIS functionalities: storage, analysis and visu-
alization. However, since they lack of an OLAP
Server, they do not implement advanced OLAP
functionalities, such as derived measures, complex
hierarchies, etc.. Consequently, GIS dominant
solutions limit spatio-multidimensional analysis
capabilities. To best of our knowledge, the only
GIS dominant solution is CommonGis (Voss et al.,
2004). It is a geovisualization system extended to
support multidimensional databases. It provides
multi-criteria functionalities, and spatial analy-
sis and visualization techniques for the analysis
of spatio-temporal data, using temporal series.
CommonGIS has been adapted to analysis of
spatio-multidimensional datasets, where spatial
information is used as analysis axes. The user
interface is flexible and interactive. It offers spatio-
multidimensional drill and cut operators, and some
advanced geovisualization techniques.
Business Information Warehouse is integrated
with a cartographic visualization tool.
Silva et al. (2006) present a Web-based SO-
LAP solution, whose principal feature is the use
of geographic Web services for the definition of
GeoMDQL. GeoMDQL is a new query language
for spatial data warehouses. It extends the OLAP
language of Microsoft (MDX) with spatial types.
The prototype is based on OLAP Server Mondrian,
which has been modified to handle GeoMDQL
queries, and OLAP client JPivot, which is coupled
with an interactive map. In Sampaio et al. (2006),
the authors describe a Web-based SOLAP sys-
tem which handles spatial measures and allows
querying spatial data warehouses using drill
and cut operators on spatial dimensions. This
solution presents a Web interface composed of a
cartographic component and a text zone to define
spatio-multidimensional queries.
Finally Shekhar et al. (2001) and Han et al.
(1997) introduce data mining techniques into
OLAP systems, and Pourabbas & Rafanelli (2002)
use visual languages. In particular, Shekhar et
al., (2001) develop the cube operator extending
aggregation of numerical data to spatial data. The
Web-based prototype is based on this operator, and
it is especially conceived for the observation and
the discovery of spatio-temporal trends. In Pourab-
bas & Rafanelli (2002) a visual language is used
to query spatial data warehouses. This approach
allows the user to formulate slice operations using
an iconic language.
OLAP Dominant Solutions
OLAP dominant solutions provide all advanced
multidimensional analysis functionalities thanks
to an OLAP system. On the contrary, GIS func-
tionalities are reduced to simple cartographic
visualization and selection of geographic objects
(Bédard et al., 2005). OLAP dominant solutions
can be grouped into two classes: tools using static
maps (Stolte et al., 2003; Colonnese et al., 2005;
Mohraz, 2000) and tools using interactive maps
(Silva et al., 2006; Sampaio et al., 2006; Shek-
har et al., 2001; Han et al., 1997; Pourabbas &
Rafanelli, 2002).
Polaris system (Stolte et al., 2003) allows the
visualization of alphanumeric measures, using
non-interactive maps incorporated into the cells
of the pivot table. PostGeOLAP (Colonnese et al.,
2005) is an open source SOLAP tool supporting
numerical measures and spatial dimensions. It
provides a set of methods to create spatio-mul-
tidimensional databases and materialize spatial
views. In Mohraz (2000) the OLAP system SAP's
OLAP-GIS Integrated Solutions
OLAP-GIS integrated solutions combine GIS
and OLAP functionalities. GIS analysis and vi-
sualization functionalities enrich and complete
OLAP navigation and visualization functionalities,
allowing a real and effective analysis of spatial
data warehouses. Some OLAP-GIS integrated
solutions have been developed (Rivest et al., 2005;
Scotch & Parmanto, 2006; Matias & Moura-Pires,
2005; Escribano et al., 2007).
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