SSAS：概念梳理

Dimension Objects

原文

A simple Dimension object is composed of basic information, attributes, and hierarchies.

Basic information includes the name of the dimension, the type of the dimension, the data source, the storage mode, and others.

Attributes define the actual data in the dimension.

Attributes do not necessarily belong to a hierarchy, but hierarchies are built from attributes.

A hierarchy creates ordered lists of levels, and defines the ways a user can explore the dimension.

• Attributes and Attribute Hierarchies
• Attribute Relationships

 

原文

Dimensions are a fundamental component of cubes.

Dimensions organize data with relation to an area of interest, such as customers, stores, or employees, to users.

Dimensions in Analysis Services contain attributes that correspond to columns in dimension tables.

These attributes appear as attribute hierarchies and can be organized into user-defined hierarchies, or can be defined as parent-child hierarchies based on columns in the underlying dimension table.

Hierarchies are used to organize measures that are contained in a cube.

Cube Objects

原文

A simple Cube object is composed of: basic information, dimensions, and measure groups.

Basic information includes the name of the cube, the default measure of the cube, the data source, the storage mode, and others.

The Dimensions collection contains the actual set of dimensions used in the cube from the database dimensions colection.

All dimensions have to be defined in the dimensions collection of the database before being referenced in the cube.

Private dimensions are not available in Microsoft SQL Server Analysis Services.

Measure groups are sets of measures in the cube.

A measure group is a collection of measures that have a common data source view and a common set of dimensions.

A measure group is the unit of process for measures; measure groups can be processed individually and then browsed.

• Actions (Analysis Services - Multidimensional Data)
• Aggregations and Aggregation Designs
• Calculations
• Cube Cells (Analysis Services - Multidimensional Data)
• Cube Properties - Multidimensional Model Programming
• Cube Storage (Analysis Services - Multidimensional Data)
• Cube Translations
• Dimension Relationships
• Key Performance Indicators (KPIs) in Multidimensional Models
• Measures and Measure Groups
• Partitions (Analysis Services - Multidimensional Data)
• Perspectives

 

Dimension Relationships 維度間的關係

• Regular：當Dimension表的鍵列直接鏈接到Fact表時，多維數據集維度（Cube Dimension）和度量值組（Measures Group）之間存在常規維度關係。 此直接關係基於底層關係數據庫中的主鍵 - 外鍵關係，但也可能基於在數據源視圖中定義的邏輯關係。 常規維關係表示傳統星型模式設計中維度表和事實表之間的關係。
• Reference：當Dimension表的鍵列經過另外一個Dimension表中的鍵間接鏈接到Fact表時，Cube Dimension和度量值組（Measures Group）之間存在引用維度關係（Reference Relationship）。
• Fact Dimension：一般稱爲簡併維度（degenerate dimensions），是從Fact表中的屬性列而不是Dim表中的屬性列構建的標準維度。 （一般指Fact表與Fact表有直接的關聯關係）
• Many to Many：在大多數維度中，每一個事實鏈接到一個且僅一個維度成員，而且單個維度成員能夠與多個事實相關聯。 在關係數據庫術語中，這被稱爲一對多關係。 然而，將單個事實鏈接到多個維度成員一般是有用的。 例如，銀行客戶可能具備多個賬戶（支票，儲蓄，信用卡和投資賬戶），而且賬戶也能夠具備聯合或多個全部者。 （一般須要在邏輯關係中，兩張表中間使用一張表做爲多對多的關聯關係表）