In the former case, a cube is stored either in a multidimensional
array or in a hash table (e.g., SQL Server) or as the value of a binary large object
(e.g., Oracle) or as another specialized data structure like Quad tree or K-D tree. In
the latter case, a cube is implemented as the set of relational tables, some of them
represent dimensions, and are called dimension tables, while others store values of
measures, and are called fact tables. Two basic types of ROLAP schemas are used
for an implementation of a cube, that is, a star schema and a snowflake schema
(Chaudhuri & Dayal, 1997). The efficiency of executing OLAP queries strongly
depends on an implementation data model and the type of the ROLAP schema used.
Therefore, a lot of work is being spent on developing DW design methods (e.g.,
Adamson & Venerable, 1998; Kimball, Reeves, Ross, & Thornthwaite, 1998; Luj??n-
Mora & Trujillo, 2004) and on modeling dimensions (e.g., Hurtado & Mendelzon,
2002; Letz, Henn & Vossen, 2002).
Having designed and implemented a DW one has to load it with data. The process of
loading data into a DW is organized into several steps that include: (1) reading data
from data sources, (2) transforming data into a common data model, (3) cleansing
data in order to remove inconsistencies, duplicates, and null values, (4) integrating
cleansed data into one set, (5) computing summaries, and (6) loading data into a
DW.
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