A popular conceptual model that influences the front-end
tools, database design, and the query engines for OLAP is the
multidimensional view of data in the warehouse. In a
multidimensional data model, there is a set of numeric
measures that are the objects of analysis. Examples of such
measures are sales, budget, revenue, inventory, ROI (return
on investment). Each of the numeric measures depends on a
set of dimensions, which provide the context for the measure.
For example, the dimensions associated with a sale amount
can be the city, product name, and the date when the sale was
made. The dimensions together are assumed to uniquely
determine the measure. Thus, the multidimensional data
views a measure as a value in the multidimensional space of
dimensions. Each dimension is described by a set of
attributes. For example, the Product dimension may consist of
four attributes: the category and the industry of the product,
year of its introduction, and the average profit margin. For
example, the soda Surge belongs to the category beverage
and the food industry, was introduced in 1996, and may have
an average profit margin of 80%. The attributes of a
dimension may be related via a hierarchy of relationships. In
the above example, the product name is related to its category
and the industry attribute through such a hierarchical
relationship.