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.