Components of multidimensional data model

The multidimensional data model is composed of logical cubes, measures, dimensions, hierarchies, levels, and attributes.

Below is the definition of the components of multidimensional data model:

• Cubes: Logical cubes provide a means of organizing measures that have the same shape, that is, they have the exact same dimensions. Measures in the same cube have the same relationships to other logical objects and can easily be analyzed and displayed together.
• Measures: Measures populate the cells of a logical cube with the facts collected about business operations. Measures are organized by dimensions, which typically include a Time dimension.
• Dimensions: contain a set of unique values that identify and categorize data. They form the edges of a logical cube, and thus of the measures within the cube.
• Hierarchy: A hierarchy is a way to organize data at different levels of aggregation. In viewing data, analysts use dimension hierarchies to recognize trends at one level, drill down to lower levels to identify reasons for these trends, and roll up to higher levels to see what affect these trends have on a larger sector of the business.
• Level: represents a position in the hierarchy.
• Attribute: An attribute provides additional information about the data.