Dylan’s BI Study Notes

My notes about Business Intelligence, Data Warehousing, OLAP, and Master Data Management

Dimensional Hierarchy

1. Dimensional Hierarchy in OBIEE

Dimensions are categories of attributes by which the business is defined. Common dimensions are time periods, products, markets, customers, suppliers, promotion conditions, raw materials, manufacturing plants, transportation methods, media types, and time of day. Within a given dimension, there may be many attributes. For example, the time period dimension can contain the attributes day, week, month, quarter, and year. Exactly what attributes a dimension contains depends on the way the business is analyzed.
A dimensional hierarchy expresses the one-to-many relationships between attributes.

Given a sample time dimension, consider the hierarchies it implies, as shown below:

Time hierarchy

With this sample time dimension, days may aggregate, or roll up, into weeks. Months may roll up into quarters, and quarters into years. When one attribute rolls up to another, it implies a one-tomany relationship. The total of all the hierarchy definitions in this sample time dimension make up this time dimension.

2. Dimensional Hierarchy in OLAP Analytical Workspace:

A hierarchy is a way to organize data at different levels of aggregation. In viewing data, analysts use dimension hierarchies to recognize trends at one level, drill down to lower levels to identify reasons for these trends, and roll up to higher levels to see what affect these trends have on a larger sector of the business.

There are two types of hierarchies:

  • Level-based Hierarchy: Level-based dimensions that use parent-child relationships to group members into levels. Most dimensions are level-based.
  • Value-based Hierarchy: Value-based dimensions that have parent-child relationships among their members, but these relationships do not form meaningful levels.

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