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Oracle Tips
by Burleson Consulting
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The Data Warehouse Development Life Cycle
Online Analytical Processing and Oracle
Simulation Of Cubic Databases (Dimensionality)
Keep in mind that dimensions may be hierarchical in nature, adding
further confusion. A time dimension, for example, may be represented
as a hierarchy with year, quarter, month, and day. Each of these
levels in the dimension hierarchy may have its own values. In other
words, a cubic representation with time as a dimension may be viewed
in the following two ways:
* A series of cubes--one for year, another for quarter, and another
for full_date.
* A five-dimension table.
MDDBs are most commonly used with data that is a natural fit for
pivot tables, and it should come as no surprise that most MDDB sites
are used with finance and marketing applications. Unfortunately,
most multidimensional databases do not scale up well for warehouse
applications. For example, the largest supported database for
Essbase is about 20 GB, whereas data warehouses with sizes measured
in terabytes are not uncommon.
It is important to note that defining aggregation of a
multidimensional database is no different than defining aggregate
tables for a relational database. At load time, the database will
still need to compute the aggregate values. MDDBs also employ the
concept of sparse data. Because data is aggregated and pre-sliced,
some cells on a cube may not contain data. For example, consider a
cube that tracks sales of items across a large company. The cells
representing sales of thermal underwear would be null for Hawaii,
and the sales of surfboards in Wyoming would also be null. Nearly
all of the product offerings are able to maintain a mechanism for
compressing out these types of null values.
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