What are Dimensions ?

Contains Hierarchies. For example Location could contain, country, pin code, state, city, etc.

Conformed Dimensions

Conformed dimensions is the very fact to which it relates. This dimension is used in more than one-star schema or Datamart.

Outrigger Dimensions

A dimension may have a reference to another dimension table. These secondary dimensions called outrigger dimensions. This kind of Dimensions should be used carefully.

Shrunken Rollup Dimensions

Shrunken Rollup dimensions are a subdivision of rows and columns of a base dimension. These kinds of dimensions are useful for developing aggregated fact tables.

Dimension-to-Dimension Table Joins

Dimensions may have references to other dimensions. However, these relationships can be modeled with outrigger dimensions.

Role-Playing Dimensions

A single physical dimension helps to reference multiple times in a fact table as each reference linking to a logically distinct role for the dimension.

Junk Dimensions

It a collection of random transactional codes, flags or text attributes. It may not logically belong to any specific dimension.

Degenerate Dimensions

Degenerate dimension is without corresponding dimension. It is used in the transaction and collecting snapshot fact tables. This kind of dimension does not have its dimension as it is derived from the fact table.

Swappable Dimensions

They are used when the same fact table is paired with different versions of the same dimension.

Step Dimensions


Sequential processes, like web page events, mostly have a separate row in a fact table for every step in a process. It tells where the specific step should be used in the overall session.

The fact and dimension tables are built by using the star schema method of data warehouse design. They are stored in the same database repository as the TRIRIGA applications.

The diagram shows the space fact with five facts, including space capacity, total headcount, remaining capacity, space area, and allocated area. The space fact also references seven dimensions, including space, building, location, geography, space class, building class, and building tenure. The dimensions in the fact table link the facts to the corresponding dimension tables. Some dimensions are hierarchical, such as location and geography and others are not, such as space and building.

Flat hierarchy tables are used to identify the children of a selected business object. Flat hierarchy tables enable the metric query engine to browse hierarchical modules, business objects, and classifications.

Using the example, if you wanted to identify all of the geographies that are children of North America, you first look up the North America SPEC_ID in the first column of the geography flat example table. Then, you might use the level number for North America, which is 2, to determine the filter column. By using the SPEC_ID and level number, you can identify all the geographies that are children, grandchildren, or any level below North America.

Facts and dimensions are components of a dimensional data model. Dimensionally modeled data works well with crosstab and graphical reports (such as charts and maps).

Facts are fields with a numeric data type (such as Currency, Integer, Decimal) that can be aggregated and analyzed. For each fact that is selected for inclusion in the dimensional model, you can also use the Fact Types setting. It globally controls the types of aggregations that can be created for each configured fact field (see Platform Reporting Framework folder settings).

Dimensions include enumerated fields, date fields, and dependent picklists that can be used by report authors as business filters and grouping fields.

You can control which facts and dimensions are represented in the dimensional namespace for each object type that can be used by report authors in reports.

If an object type includes fields with a numeric data type (such as Currency, Integer, Decimal) then these fields are automatically listed in the Facts table for selection.

You can enable and disable enumerated fields and dependent picklists as dimensions.

When date fields are used as dimensions in reports, users can drill down through a date hierarchy from the year to a specific quarter, month, and/or day.

A dimension is a structure that categorizes facts and measures in order to enable users to answer business questions. Commonly used dimensions are people, products, place and time.

In a data warehouse, dimensions provide structured labeling information to otherwise unordered numeric measures. The dimension is a data set composed of individual, non-overlapping data elements. The primary functions of dimensions are threefold: to provide filtering, grouping and labelling.

These functions are often described as "slice and dice". A common data warehouse example involves sales as the measure, with customer and product as dimensions. In each sale a customer buys a product. The data can be sliced by removing all customers except for a group under study, and then diced by grouping by product.

A dimensional data element is similar to a categorical variable in statistics