Database Reference
In-Depth Information
These solutions store spatial multidimensional
data using a Spatial DBMS. Spatial data warehous-
es allow modelling complex spatial hierarchies,
topological intersection relationships, and spatial/
geographic measures. Star and snowflake schemas
are modified to handle spatial data (Stefanovic et
al., 2000; Malinowsky & Zimányi, 2007).
(S)OLAP Servers implement spatio-multidi-
mensional operators explicitly taking into account
spatial members' geometry to define cutting
spatial predicates and to perform drill operators.
They allow defining spatial members, hierarchies,
measures and derived measures.
SOLAP clients improve OLAP clients thanks to
cartographic visualization and interaction function-
alities. They combine and synchronize tabular and
graphic visualization components with interactive
maps (Rivest et al., 2005). SOLAP operators are
available through the simple interaction with maps.
Each spatial level corresponds to a cartographic
layer. Performing (S)OLAP operators on the pivot
table and/or on the map will result in the calculation
and displaying of a new clickable map (interactive
map), merging different layers according to the dif-
ferent granularities selected for the spatial members,
and displaying aggregated and/or detailed measures
with visual variables and/or graphic displays (i.e.
bars, pies, etc.) (Rivest et al., 2005).
For instance, considering the application of
figure 2, one should be able to show and to inter-
act with a map displaying average and minimum
values for selected months and pollutants at the
granularity of cities for one department, and at
department level for the rest of the map.
In order to take advantage from human visual
perception capabilities, interactive maps should
be coupled with advanced geovisualization tech-
niques such as MultiMaps, Space-Time Cubes, etc.
(MacEachren et al., 2004). Finally, GIS operators
are available through the cartographic component.
Indeed, decision makers should be able to custom-
ize their maps, add layers, query the spatial data
warehouse, use spatial analysis tools, etc.
Rivest et al. (2005) present a SOLAP tool
which permits to enrich the elements of the spa-
tial data warehouse with multimedia documents
such as photos, videos, etc.. SOVAT (Scotch &
Parmanto, 2006) extends SOLAP functionalities
with (spatial) data mining tools (i.e. clustering,
etc.). It provides also statistic and spatial analysis
methods. The SOLAP tool described in Matias
& Moura-Pires (2005) allows overlapping two
hypercubes using their spatial dimensions. PIET
(Escribano et al., 2007) is a Web-based SOLAP
solution providing a new query processing meth-
od. Spatial members are first organized into open
convex polygons, and then the overlay of these
polygons is used to answer to multidimensional
queries using user-defined window queries.
In the following paragraph we describe our
OLAP-GIS integrated solution.
GeWOlap: Spatio-
Multidimensional Analysis Tool
GeWOlap is a Web-based system for explora-
tion and analysis of spatio-multidimensional
databases (Bimonte et al., 2006; Bimonte et al.,
2007a, Bimonte et al., 2007b). It provides ad-
vanced (S)OLAP and GIS functionalities through
a flexible interactive and user-friendly interface.
GeWOlap is based on three tier architecture.
The spatial data warehouse tier is implemented
using Oracle for its native support for spatial data
and its object-relational capabilities.
OLAP server Mondrian provides an easily
customizable, full featured (i.e. aggregate tables,
user-defined functions, complex hierarchies, MDX
support, etc.) OLAP server. It is important to notice
that no spatial extension has been integrated into
Mondrian. Spatial data is represented using simple
textual labels. In order to overcome this limitation,
GeWOlap handles spatial data in the spatial data
warehouse and client tiers, and it provides a mapping
mechanism between spatial data and their textual
representation in the OLAP Server.
The client tier is implemented using the OLAP
client JPivot to provide JSP pages, and a Java
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