![]() Value-based dimensions that have parent-child relationships among their members, but these relationships do not form meaningful levels. Level-based dimensions that use parent-child relationships to group members into levels. You can define dimensions that have any of these common forms: In a report, the dimension values (or their descriptive attributes) provide labels for the rows and columns. They form the edges of a cube, and thus of the measures within the cube. ![]() When you right-click an object, you get a choice of menu items with appropriate actions for that object.Īnalytic Workspace Manager has a full online Help system, which includes context-sensitive Help.ĭimensions are lists of unique values that identify and categorize data. When you select an object in the navigation tree, the property sheet to the right provides detailed information about that object. It contains menus, a toolbar, a navigation tree, and property sheets. This chapter assumes that you have a star, snowflake, or other relational schema that supports dimensional objects.įigure 3-1 shows the main window of Analytic Workspace Manager. You must have SELECT privileges on the relational data sources so you can load the data into the dimensions and cubes. You can load data from these sources in the database: Load data from relational tables into those objects.Ĭreate materialized views that can be used by the database refresh system.Īutomatically generate relational views of the dimensional objects. Instantiate that model as dimensional objects. Using Analytic Workspace Manager, you can:ĭevelop a dimensional model of your data. The data loading process transforms the data from a relational format into a dimensional format. Afterward, you can map these dimensional objects to their relational data sources. The first step in that transformation is defining the cubes, measures, dimensions, levels, hierarchies, and attributes. Populating dimensional objects involves a physical transformation of the data. This data store can stand alone or store summary data as part of a relational data warehouse. ![]() Your goal in using Analytic Workspace Manager is to create a dimensional data store that supports business analysis. While investigating your source data, you may decide to create relational views that more closely match the dimensional model that you plan to create.Īnalytic Workspace Manager is the primary tool for creating, developing, and managing dimensional objects in Oracle Database. The reports identify the levels of aggregation that interest the report consumers and the attributes used to qualify the data. You can also get insights into the dimensional model by looking at the reports currently being generated from the source data. Parent columns in the dimension tables identify the higher level dimension members.Ĭolumns in the dimension tables containing descriptions and characteristics of the dimension members identify the attributes. Primary keys in the dimension tables identify the base-level dimension members. If your source data is in a star or snowflake schema, then you have the elements of a dimensional model:ĭata columns in the fact tables correspond to measures.įoreign key constraints in the fact tables identify the dimension tables.ĭimension tables identify the dimensions. What are your measures? What are your dimensions? How can you distinguish between a dimension and an attribute in your data? You can design a dimensional model using pencil and paper, a database design software package, or any other method that suits you. In this chapter, you learn how to define them in Oracle Database, but first you should decide upon the dimensional model you want to create. Chapter 1 introduced the dimensional objects: Cubes, measures, dimensions, levels, hierarchies, and attributes.
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