Page 143 - DCAP208_Management Support Systems

Management Support Systems

Notes etc., it would be more appropriate to store all the data in one site with a homogeneous
structure that allows interactive analysis. In other words, data from the different stores
would be loaded, cleaned, transformed and integrated together. To facilitate decision-
making and multi-dimensional views, data warehouses are usually modeled by a multi-
dimensional data structure.

Example: Figure 9.3 shows an example of a three dimensional subset of a data cube
structure used for OurVideoStore data warehouse.

Figure 9.3: Multi-dimensional Data Cube Structure

Source: http://webdocs.cs.ualberta.ca/~zaiane/courses/cmput690/notes/Chapter1/
The figure shows summarized rentals grouped by film categories, then a cross table of
summarized rentals by film categories and time (in quarters). The data cube gives the
summarized rentals along three dimensions: category, time and city. A cube contains cells
that store values of some aggregate measures (in this case rental counts), and special cells
that store summations along dimensions. Each dimension of the data cube contains a
hierarchy of values for one attribute.
Because of their structure, the pre-computed summarized data they contain and the
hierarchical attribute values of their dimensions, data cubes are well suited for fast
interactive querying and analysis of data at different conceptual levels, known as On-Line
Analytical Processing (OLAP). OLAP operations allow the navigation of data at different
levels of abstraction, such as drill-down, roll-up, slice, dice, etc. Figure 9.4 illustrates the
drill-down (on the time dimension) and roll-up (on the location dimension) operations.
Figure 9.4: Drill-down (on the Time Dimension) and
Roll-up (on the Location Dimension) Operations

Source: http://webdocs.cs.ualberta.ca/~zaiane/courses/cmput690/notes/Chapter1/

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