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Execution-MiH ENCYCLOPEDIA →
Enterprise Intelligence →
SECTION - Data Analysis/OLAP →
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CHAPTER - Additivity and Aggregation of Measures-Facts in OLAP Analysis
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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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Topics
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Additivity of Measures-Facts
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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.
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Non-Additive Measures-Facts
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Non-Additivity is that when you cannot use a sum operator to generate the needed aggregation.
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Semi-Additive Measures-Facts
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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.
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All Chapters in "Data Analysis/OLAP." Section
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