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   Business Hierarchies in OLAP and Data Warehouse  

BiPM ENCYCLOPEDIA  →   Intelligent Enterprise →  SECTION - Data Analysis/OLAP → 

CHAPTER -  Additivity and Aggregation of Measures-Facts in OLAP Analysis

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.


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Additivity of Measures-Facts   

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. Other aggregations on measures-facts are when you use operators like Average, Maximum and Minimum.
 

Non-Additive Measures-Facts   

Non-Additivity is that when you cannot use a sum operator to generate the needed aggregation.
 

Semi-Additive Measures-Facts   

Semi-Additivity is when you can have a measure aggregated on a certain dimension, but not all the dimensions. Another phrase for semi-additivity is when you have the summarization with an index of in-accuracy.
 


   Business Hierarchies in OLAP and Data Warehouse  

All Chapters in "Data Analysis/OLAP." Section
 Online Analytic Processing (OLAP)-Overview →  Basic Data Analysis Types- Building Blocks →  Advanced Data Analysis Types- Building Blocks →  Business Hierarchies in OLAP and Data Warehouse →  Additivity and Aggregation of Measures-Facts in OLAP Analysis → 

 
 
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Data Analysis/OLAP