|
Additivity and correct aggregation methods application is fundamental to the success of Business Intelligence. The most common mistakes the modelers and designers make is on - Setting the Right Hierarchies AND Establishing Right Additivity and aggregation rules. You need to go through the chapter of business dimensional hierarchies, before you go through this chapter. Additivity of a measure is, when you are able to apply the sum operator across all the dimensions, through the entire hierarchy path. Other aggregations on measures-facts are, when you use operators like Average, Maximum and Minimum. The topics in this chapter are focused on the areas which have additivity OR aggregation constraints. For measures, which do not fall into these constraints are considered Additive..
The OLAP tools now-a-days have some capability to automatically enforce the correct additivity and Averaging rules, given the hierarchy and the type of measure. However, the burden is finally on the modelers and designers.
Before we move further, let's take a look at some more aspects, which will be useful:
Completeness of Hierarchy:
This basically means that all the possible instances of a hierarchy path to be available, to make it complete. It means that there should be no missing data in the tables. For example, if you have country and continent level in the location dimension, one should expect that all the countries in the Europe continent should exist in the tables. Otherwise your summarization for the continent may not work.
Classification vs. descriptive attributes:
A classification attribute of a dimension is the attribute, on which the aggregation takes place. A dimensional attribute is the one, which plays the role of a descriptor and is not the basis of aggregation. You will see that OLAP includes all classification attributes and some descriptive attributes.
|
