GeoBI Architecture Based on Free Software: Experience and Review - Geographical Information Systems: Trends and Technologies - page 247

us to assess the value of the research achievements related SDWs, spatial
ETL, and SOLAP with the existing practice.
The first section briefly describes the basic concepts required to
understand the remaining sections. The following section introduces
our case study, which we use throughout this chapter to present our
implementation of a SDW and SOLAP. Then, we synthesize the existing
research related to SDWs, spatial ETL, and SOLAP, referring it to challenges
found in practice and presenting the possible new research topics. Finally,
we present conclusions.

Basic Concepts

A DW is commonly used to store historical data that can be analyzed using
OLAP tools. Both systems rely on a multidimensional view of data that at
the abstract level is called a cube and at the implementation level is often
referred to as star and snowfl ake schemas. These kinds of schemas are
organized into fact tables related to several dimension tables.
A fact table ( Sales in Fig. 1) represents the focus of analysis (e.g., analysis
of stores' sales) and typically includes attributes called measures . These are
usually numeric values (e.g., Quantity and Amount in Fig. 1) that facilitate
a quantitative evaluation of various aspects of interest. Dimensions (e.g.,
Product , Time , or Store in Fig. 1) are used to see the measures from different
perspectives, e.g., according to geographic distribution of stores. Dimensions
typically include attributes, some of which may form hierarchies . The
difference between star and snowfl ake schemas resides in how hierarchies

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