Multidimensional Data Model

Topics: Data warehouse, Star schema, Data warehousing Pages: 4 (1086 words) Published: February 21, 2013
Data warehouses and OLAP tools are based on a multidimensional data model. This model views data in the form of a data cube. FROM TABLES TO DATA CUBES
What is a data cube?
A data cube allows data to be modeled and viewed in multiple dimensions. It is defined by dimensions and facts. In general terms, dimensions are the perspectives or entities with respect to which an organization wants to keep records. Each dimension may have a table associated with it, called a dimension table, which further describes the dimension. Facts are numerical measures. The fact table contains the names of the facts, or measures, as well as keys to each of the related dimension tables. Example:

2-D representation, the sales for Vancouver are shown with respect to the time dimension (organized in quarters) and the item dimension (organized according to the types of items sold). The fact, or measure displayed is dollars sold.

Now, suppose that we would like to view the sales data with a third dimension. For instance, suppose we would like to view the data according to time, item, as well as location.
The above tables show the data at different degrees of summarization. In the data warehousing research literature, a data cube such as each of the above is referred to as a cuboid. Given a set of dimensions, we can construct a lattice of cuboids, each showing the data at a different level of summarization, or group by (i.e., summarized by a different subset of the dimensions). The lattice of cuboids is then referred to as a data cube. The following figure shows a lattice of cuboids forming a data cube for the dimensions time, item, location, and supplier.

The cuboid which holds the lowest level of summarization is called the base cuboid. The 0-D cuboid which holds the highest level of summarization is called the apex cuboid. The apex cuboid is typically denoted by all.

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