Advanced Modeling - Microsoft SQL Server 2008 Analysis Services - page 91

The definition of a parent-child hierarchy is based on the data that is loaded in the key
attribute (as opposed to the typical hierarchy, which isn't dependent on data being
present in an attribute).
The hierarchy is built on a collection of attributes within attributes. The key attribute will
be divided internally into several attributes, which contain members from the various
levels. The hierarchy includes one level for each nested attribute that contains data. If
more data is introduced, the number of levels can change. The parent-child hierarchy can
be seen as a single hierarchy whose structure is determined by the data that populates it.
There are a couple of important characteristics of the parent-child hierarchy: First, it's
unbalanced; and second, it's flexible. It's independent of the parameters set for the rela-
tionships of the parent and child attributes.
Members of the key attribute for a parent-child hierarchy can be found on any level of the
hierarchy. Data members, which are usually one level below the member for which they
represent data, can also appear on any level of a parent-child hierarchy. Members on the
lowest level of the hierarchy are the only ones that don't have data members (which
would be below them, which would be impossible). Truly speaking, they are data members
of themselves.
The parent-child hierarchy is unbalanced because you can't control which member has
children and which doesn't, or what level the parent member will be on. To produce a
balanced parent-child hierarchy, the user must carefully craft the relationships of the
members so that members from the lowest level of the hierarchy are the only ones that
don't have children.
In addition, members can change their positions in the hierarchy whenever you update
the dimension. This capability makes the hierarchy flexible.
Parent-child hierarchies enable you to create a flexible data structure. However, to
construct and maintain a parent-child hierarchy requires considerably more resources
from the system. The complexity a parent-child hierarchy introduces into multidimen-
sional cubes can greatly influence the effectiveness of the model and make it difficult for
the user to understand the model. We recommend that you avoid using parent-child hier-
archies except when they are absolutely necessary.
Attribute Discretization
When we speak of the values of an attribute, we can be talking about two different types.
Discrete values are those that have clear boundaries between the values. For example, the
Gender attribute is typically considered to have only discrete values (that is, male or
female). For the majority of attributes, the possible values are naturally discrete.
Contiguous values are those for which no clear boundaries exist, where the values flow
along a continuous line. For example, a worker's wage typically falls in a range of possible
values. And, the more employees you have, the more possibilities exist in that range.
Some sets of contiguous values can be of infinite or a very large number of possibilities. It
can be difficult to work efficiently with such a wide range of values.