Overview of the OLAP Catalog - OLAP

The OLAP Catalog defines logical multidimensional objects and maps them to physical data sources. The logical objects are cubes, measures, dimensions, and so forth as described in "The Logical Multidimensional Data Model". The physical data sources are the columns of a relational table or view. A number of different warehouse configurations can be represented by OLAP Catalog metadata.

The OLAP Catalog serves these distinct functions for analytic workspaces:

  • Describes the relational tables of a star or snowflake schema so that the data can be fetched into an analytic workspace. This metadata is used only when building or refreshing the analytic workspace.
  • Describes the relational views of an analytic workspace so that the data can be queried by the BI Beans. This metadata is used only at runtime so that applications have access to the workspace data.

Thus, when you are developing an analytic workspace, you may create two sets of OLAP Catalog metadata: one for the source schema, and the other for the analytic workspace. If your analytic workspace is used by another application, such as Oracle Discoverer, then you only define OLAP Catalog metadata for your source schema. For your analytic workspace, you create an End User Layer (EUL), which is the type of metadata required by Discoverer.

The OLAP Catalog is also used to describe the relational tables of a star schema so that the data can be queried by the BI Beans. In this type of scenario, no analytic workspace is used; aggregate data is stored in materialized views.The BI Beans query metadata stored in the OLAP Catalog. Your data, whether it is stored in relational tables or in an analytic workspace, is inaccessible to applications based in these technologies unless the data is identified in the OLAP Catalog. The OLAP Catalog is also available to any other applications that want to use it.

OLAP Catalog Components

The OLAP Catalog includes the following:

  • Metadata model tables: A set of relational tables within the database that instantiate the OLAP metadata model. These tables define all the OLAP metadata objects: dimensions, measures, cubes, measure folders, and so on. Within the metadata definitions are references to the actual data sources.
  • Write API: A set of PL/SQL packages for creating and editing OLAP metadata. These packages contain procedures for inserting, updating, and deleting rows in the model tables.
  • Read API: A set of relational views within the database that provide information about the metadata registered in the model tables.

Two versions of the OLAP Catalog are currently in use, CWM1 (also called CWM-Lite) and CWM2. Each version has its own metadata model tables, write API, and read API. However, applications can query a set of union views that contains all of the OLAP Catalog metadata, regardless of the write API used to generate it.

About CWM1

CWM1 is available through the OLAP Management tool of Oracle Enterprise Manager. You can use CWM1 only to describe a schema that complies with the requirements listed in "Choosing a Tool for Creating OLAP Catalog Metadata". You can then use the OLAP Catalog to create an analytic workspace or to access the relational schema directly through the BI Beans.

You can view CWM1 metadata in the OLAP Management tool of Enterprise Manager, or in the OLAP Catalog View of Analytic Workspace Manager.

About CWM2

CWM2 is available through the BI Beans enabler in Analytic Workspace Manager and as a set of PL/SQL packages. You can use CWM2 to describe a star or snowflake schema that does not comply with the requirements for CWM1. You can use only CWM2 to define the metadata for an analytic workspace; you cannot use CWM1 for this purpose.

You can view CWM2 metadata in the OLAP Catalog View of Analytic Workspace Manager.

Steps for Creating OLAP Metadata

Whether you create OLAP metadata programmatically or by using a graphic interface, you follow the same basic steps.

To create OLAP metadata:

  1. Create logical dimensions. Specify the levels, attributes, and hierarchies associated with each one.
  2. Create logical cubes and specify their edges (dimensions).
  3. Create logical measures that represent the fact data. Associate each measure with a cube.
  4. Map the logical entities to the source data.

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