What is a Multi-dimensional Model?

Multi-dimensional model is an integral aspect of the On-line Analytical Processing which also known as OLAP.

Due to the fact that OLAP is online it provides information quickly, iterative queries are often posed during interactive sessions.

Due to the analytical nature of OLAP the queries are often complex. The multi-dimensional model is used to solve this kind of complex queries. The model is important because it applies simplicity.

This helps users understand the databases and enables software to plot a course through the databases effectively.

Multi-dimensional data models are made up of logical cubes, measures, and dimensions. Within the models you can also find hierarchies, levels, and attributes.

The straightforwardness of the model is essential due to the fact that is identifies objects that represent real world entities.

The analysts know what measures they want to see, what dimensions and attributes make the data important, and in what ways the dimensions of their work is organized into levels as well as hierarchies.

What are Logical Cubes and Logical Measures?

Let us touch on what the logical cubes and logical measures are before we move on to more complicated details.

Logical cubes are designed to organize measures that have the same exact dimensions. Measures that are in the same cube have the same relationship to other logical objects; they can easily be analyzed and shown together.

With logical measures cells of the logical cube are filled with facts collected about an organization’s operations or functions.

The measures are organized according to the dimensions, which also deals with time dimension.

Analytic databases contain outlines of historical data, taken from data in a heritage system, also those other data sources such as syndicated sources. The normally acceptable amount of historical data for analytic applications is about three years worth.

The measures are static; they are also trusted to be consistent while they are being used to help make informed decisions.

The measures are updated often, most applications update data by adding to the dimensions of a measure. These updates give users concrete historical record of a specific organizational activity for an interval. This is very productive.

Another productive strategy is that adopted by other application, which fully rebuild the data rather then perform updated.

The lowest level of a measure is called the grain. Often this level of data is never seen, even so it has a direct affect on the type of analysis that can be done.

This level also determines whether or not the analysts can obtain answers. Questions such as, when are men most likely prone to place orders for custom purchases?

Next Page: Logical Dimensions