The aim of this page is to provide a glossary of common OLAP and data warehousing terms and expressions. This list is for all terms beginning with A

Analytic Workspace

Multidimensional data is stored in analytic workspaces, where it can be manipulated by the OLAP engine in Oracle Database. Individual analytic workspaces are stored in tables in a relational schema, and they can be managed like other relational tables. An analytic workspace is owned by a particular user ID, and other users can be granted access to it. Within a single database, many analytic workspaces can be created and shared among users.

Analytic workspaces have been designed explicitly to handle multidimensionality in their physical data storage and manipulation of data. The multidimensional technology that underlies analytic workspaces is based on an indexed multidimensional array model, which provides direct cell access. This intrinsic multidimensionality affords analytic workspaces much of their speed and power in performing multidimensional analysis.

Analytic Workspace Manager

Analytic Workspace Manager (AWM) is an administrative tool used to create a multidimensional data store that supports business analysis. Analytic Workspace Manager is the primary tool for creating, developing, and managing analytic workspaces. However, Oracle Warehouse Builder can also be used to design, deploy and manage multi-dimensional data models. Note - Please refer to the OWB Wiki page for more information.

Analytic Workspace Manager can be used to do the following tasks:

Create standard form analytic workspaces that can be accessed by OracleBI Discoverer, OracleBI Spreadsheet Add-In, and custom BIBeans applications.
Create a logical multidimensional data model for transforming your data into a multidimensional data store in an analytic workspace.
Create and deploy plans for storing aggregate data to improve query response time without overly increasing the demand for data storage.
Create calculated measures, forecasts, and allocations that provide information-rich data to business analysts.
Create and schedule jobs for loading new data using the Oracle Database job queue.
Open OLAP Worksheet for direct access to the OLAP DML (Data Manipulation Language).

The main AWM window provides two views: the Model View and the Object View. You can switch between views using the View menu. In addition, there are menus, a toolbar, a navigation tree, and property sheets. When you select an object in the navigation tree, the property sheet to the right provides detailed information about that object. When you right-click an object, you get a choice of menu items with appropriate actions for that object.

Model View

The Model View enables you to define a logical dimensional model composed of dimensions, levels, hierarchies, attributes, measures, calculated measures, and measure folders. The model is stored in the analytic workspace as database standard form metadata. A drag-and-drop user interface facilitates mapping of the logical objects to columns in relational tables, views, and synonyms in Oracle Database. The source columns can be star, snowflake, or any other schema design that supports the logical model.

The image below shows the logical objects created in the GLOBAL analytic workspace:

Analytic Workspace Manager has a full online Help system, which includes context-sensitive Help.

Ancestor

A dimension member at a higher level of aggregation than a particular member. For example, in a Time dimension, the year 2007 is the ancestor of the day 06-July-07. The member immediately above is the parent. In a dimension hierarchy, the data value of the ancestor is the aggregated value of the data values of its descendants.Contrast withdescendant. See alsohierarchy,level,parent.

Attribute

An attribute provides additional descriptive information about a dimension and/or specific members within that dimension - it is a descriptive characteristic of either a single dimension member or a group of dimension members. When applied to a single member, attributes provide supplementary information that can be used for display. For example, you might have a product dimension that uses Stock Keeping Units (SKUs) for dimension members. The SKUs are an excellent way of uniquely identifying thousands of products, but are meaningless to most people if they are used to label the data in a report or a graph. You would define attributes for the descriptive labels to help business users work with the data.



Attributes can also provide additional information that is used directly for analysis. For example, when we define a Time dimension it can be useful to know the number of days (time span) in a time period and also the end date. When you define a dimension as type TIME using AWM, both these attributes (Time Span and End Date) are automatically created. These types of attributes typically apply to all members in a dimension. When applied to a group, attributes represent logical groupings that enable users to select data based on like characteristics. For example, in a database representing footwear, you can use a shoe color attribute to select all boots, sneakers, and slippers that share the same color. By adding these types attributes to your data model business users can answer questions such as: Which colors were the most popular in women's dresses in the summer of 2005? How does this compare with the previous summer?

When using AWM to define a dimension, each dimension is automatically assigned two separate attributes

• Long Description
• Short Description

In addition dimensions of type Time gain additional two attributes, time span and end date. These are a special type of attribute because they enable some additional time based calculations within the OLAP Calculation Wizard. If these attributes are present, the following time calculations are enabled:

• Prior Value
• Difference Prior Period
• % Difference Prior Period
• Future Value
• Moving Minimum
• Moving Maximum
• Moving Total
• Period to Date

.

AW XML

AWXML Java package contains classes that define an object model and instantiate it in a standard form within an analytic workspace.

The XML document can define either a complete Analytic Workspace, or individual standard form objects such as dimensions and cubes. The following list defines the key types of tags within an typical XML document used to create an analytic workspace:

• AWObject — This is a super class that describes objects that have attributes. This object is loadable from an external data source, such as relational data. Examples of AWObject include dimensions and measures.
• Attribute — This is an object that describes the characteristic features of another object.
• Dimension — This is an ordered list of dimension members. Dimension is the core of the multi-dimensional model.
• Cube — This is a logical object that describes the dimensionality of objects. Cubes are composed of one or more dimensions.
• Hierarchy — A hierarchy is a way of structuring dimension members. At the bottom of a hierarchy lies the detail level. If a dimension member is at the top of the hierarchy, then the parent value is null.
• MemberSelection — This is an ordered list of dimension members. A MemberSelection is useful for identifying a specific portion of a cube.
• Level — This is a MemberSelection that describes a portion of a hierarchy. In a level, no parent can be a child of another member of the same level.
• HierarchyLevelAssociation — This is another abstract object that provides a link between a hierarchy and its levels. The end-user does not commonly work with this object.
• Measure — A Measure is the object that holds the user's data. The dimensions, hierarchies etc. all provide the logical framework that holds the Measure.
• SolveDefinition — This represents a set of computations to be applied to one or more measures. SolveDefinition represents the real value of the Oracle OLAP system.

What does an AWXML document look like? The is an example of an XML document that is used to remove a dimension from an analytic workspace.