3 Creating Dimensions and Cubes

Adding Functionality to Dimensional Objects

In addition to the basic steps, you can add functionality to the cubes in these ways:

• Develop custom cube scripts to customize the builds.

  See "Creating and Executing Custom Cube Scripts".

• Generate materialized views that support automatic refresh and query rewrite.

  See "Adding Materialized View Capability to a Cube".

• Support multiple languages by adding translations of metadata and attribute values.

  See "Supporting Multiple Languages".

• Define measure folders to simplify access for end users.

  See "Defining Measure Folders".

When Does Analytic Workspace Manager Save Changes?

Analytic Workspace Manager saves changes automatically that you make to the analytic workspace. You do not explicitly save your changes.

Saves occur when you take an action such as these:

• Click OK or the equivalent button in a dialog box.

  For example, when you click Create in the Create Dimension dialog box, the dimension is committed to the database.

• Click Apply in a property sheet.

  For example, when you change the labels on the General property page for an object, the change takes effect when you click Apply.

Creating Levels

For business analysis, data is typically summarized by level. For example, your database may contain daily snapshots of a transactional database. Days are the base level. You might summarize this data at the weekly, quarterly, and yearly levels.

Levels have parent-child or one-to-many relationships, which form a level-based hierarchy. For example, each week summarizes seven days, each quarter summarizes 13 weeks, and each year summarizes four quarters. This hierarchical structure enables analysts to detect trends at the higher levels, then drill down to the lower levels to identify factors that contributed to a trend.

For each level that you define, you must identify a data source for dimension members at that level. Members at all levels are stored in the same dimension. In the previous example, the Time dimension contains members for weeks, quarters, and years.

To create a level: 

1. Expand the folder for the dimension.

2. Right-click Levels, then choose Create Level.

   The Create Level dialog box is displayed.

3. Complete the General tab of the Create Level dialog box.

4. Click Create.

   The level appears as an item in the Levels folder.

Figure 3-3 shows the creation of the Class level for the Product dimension.

Figure 3-3 Creation of the Class Level

Description of "Figure 3-3 Creation of the Class Level"

Creating Hierarchies

Dimensions can have one or more hierarchies. They can be level based or value based.

Level-Based Hierarchies

Most hierarchies are level based. Analytic Workspace Manager supports these common types of level-based hierarchies:

• Normal hierarchies consist of one or more levels of aggregation. Members roll up into the next higher level in a many-to-one relationship, and these members roll up into the next higher level, and so forth to the top level.

• Ragged hierarchies contain at least one member with a different base, creating a "ragged" base level for the hierarchy. Ragged hierarchies are not supported for cube materialized views.

• Skip-level hierarchies contain at least one member whose parents are multiple levels above it, creating a hole in the hierarchy. An example of a skip-level hierarchy is City-State-Country, where at least one city has a country as its parent (for example, Washington D.C. in the United States).

  In relational source tables, a skip-level hierarchy may contain nulls in the level columns. Skip-level hierarchies are not supported for cube materialized views.

Multiple hierarchies for a dimension typically share the base-level dimension members and then branch into separate hierarchies. They can share the top level if they use all the same base members and use the same aggregation operators. Otherwise, they need different top levels to store different aggregate values. For example, a Customer dimension may have multiple hierarchies that include all base-level customers and are summed to a shared top level. However, a Time dimension with calendar and fiscal hierarchies must aggregate to separate Calendar Year (January to December) and Fiscal Year (July to June) levels, because they use different selections of base-level members.

Value-Based Hierarchies

You may also have dimensions with parent-child relations that do not support levels. For example, an employee dimension might have a parent-child relation that identifies each employee's supervisor. However, levels that group first-, second-, and third-level supervisors and so forth may not be meaningful for analysis. Similarly, you might have a line-item dimension with members that cannot be grouped into meaningful levels. In this situation, you can create a value-based hierarchy defined by the parent-child relations, which does not have named levels. You can create value-based hierarchies only for dimensions that use the source keys, because surrogate keys are formed with the names of the levels.

To create a hierarchy: 

1. Expand the folder for the dimension.

2. Right-click Hierarchies, then choose Create Hierarchy.

   The Create Hierarchy dialog box is displayed.

3. Complete the General tab of the Create Hierarchy dialog box.

   Click Help for information about these choices.

4. Click Create.

   The hierarchy appears as an item in the Hierarchies folder.

Figure 3-4 shows the creation of the Primary hierarchy for the Product dimension.

Figure 3-4 Creation of the Product Primary Hierarchy

Description of "Figure 3-4 Creation of the Product Primary Hierarchy"

Creating Attributes

Attributes provide information about the individual members of a dimension. They are used for labeling crosstabular and graphical data displays, selecting data, organizing dimension members, and so forth.

User-Defined Attributes

You can create additional "User" attributes that provide supplementary information about the dimension members, such as the addresses and telephone numbers of customers, or the color and sizes of products.

To create an attribute: 

1. Expand the folder for the dimension.

2. Right-click Attributes, then choose Create Attribute.

   The Create Attribute dialog box is displayed.

3. Complete the General tab of the Create Attribute dialog box.

   Some attributes apply to all dimension members, and others apply to only one level. Your selection in the Apply Attributes To box controls the mapping of the attribute to one column or to multiple columns.

   Click Help for information about these choices.

4. To change the data type from the default choice of VARCHAR2, complete the Implementation Details tab.

5. Click Create.

   The attribute appears as an item in the Attributes folder.

Figure 3-5 shows the creation of the Marketing Manager attribute for the Product dimension. Notice that this attribute applies only to the Item level.

Figure 3-5 Creation of the Product Marketing Manager Attribute

Description of "Figure 3-5 Creation of the Product Marketing Manager Attribute"

Mapping Dimensions

Mapping identifies the relational data source for each dimensional object. After mapping a dimension to a column of a relational table or view, you can load the data. You can create, map, and load each dimension individually, or perform each step for all dimensions before proceeding to the next step.

SQL Data Types for Dimensions

You can map dimensions and levels to columns having these SQL data types, which are converted to text during a data load:

• VARCHAR2

• NVARCHAR2

• NUMBER

• INTEGER

• DECIMAL

• CHAR

• NCHAR

• DATE

• TIMESTAMP

• TIMESTAMP WITH TIMEZONE

• TIMESTAMP WITH LOCAL TIMEZONE

You can map attributes to the same data types as cubes and measures, as described in "Data Types".

Dimension Mapping Window

The mapping window has a tabular view and a graphical view. You can switch between the two views, using the icons at the top of the canvas.

• Tabular view: Drag-and-drop the names of individual columns from the schema navigation tree to the rows for the dimensional objects.

• Graphical view: Drag-and-drop icons, which represent tables and views, from the schema navigation tree onto the mapping canvas. Then draw lines from the columns to the dimensional objects.

You can use the OLAP expression syntax when mapping dimensions in the tabular view. This capability enables you to create the top level of a dimension without having a source column in the dimension table.

You can also map attributes from different tables. OLAP automatically joins the tables on columns with the same name.

Click Help on the Mapping window for more information.

To map a dimension:  

1. In the navigation tree, expand the dimension folder and click Mappings.

   The Mapping window contains a schema navigation tree on the left and a mapping table for the dimension with rows for the levels and their attributes. This is the tabular view.

2. For normalized dimension tables, select Snowflake Schema for the Type of Dimension Table.

3. To enlarge the Mapping Window, drag the divider to the left.

4. In the schema tree, expand the tables, views, or synonyms that contain the dimension members and attributes.

5. Drag-and-drop the source columns onto the appropriate cells in the mapping table for the dimension.

6. After you have mapped all levels and attributes, click Apply.

7. Drag the divider back to the right to reveal the navigation tree.

Figure 3-6 shows the Product dimension mapped in the tabular view. The arrow highlights how the PRODUCT_DIM.ITEM_BUYER column maps to the PRODUCT.ITEM.BUYER attribute.

Figure 3-6 Product Dimension Mapped in Tabular View

Description of "Figure 3-6 Product Dimension Mapped in Tabular View"

To map a top level without a relational source:  

1. Create the dimension and its levels (including the top level), hierarchies, and attributes.

2. Map the dimension as described previously for all but the top level.

3. Enter an expression in the OLAP expression syntax for the top level.

Example 3-1 Creating a Top Level for the Global Time Dimension

This example shows a top level for all years in the Time dimension. The mapping expressions used for a Total level (that is, all years) in the Time dimension might look like this:

Member: 'TOTAL' 
LONG_DESCRIPTION: 'Total'
SHORT_DESCRIPTION: 'Total' 
END_DATE: TO_DATE('31-Dec-2007', 'dd-mon-yyyy')
TIME_SPAN: 3646 

Member, LONG_DESCRIPTION, and SHORT_DESCRIPTION are set to literal strings, END_DATE uses the TO_DATE function, and TIME_SPAN is set to a number.

Source Data Query

You can view the contents of a particular source column without leaving the mapping window. The information is readily available, eliminating the guesswork when the names are not adequately descriptive.

To see the values in a particular source table or view: 

1. Right-click the source object in either the schema tree or the graphical view of the mapping canvas.

2. Choose View Data from the shortcut menu.

Figure 3-7 shows the data stored in the PRODUCT_DIM table.

Figure 3-7 Data in the PRODUCT_DIM Table

Description of "Figure 3-7 Data in the PRODUCT_DIM Table"

Loading Data Into Dimensions

Analytic Workspace Manager provides several ways to load data into dimensional objects. The quickest way when developing a data model is using the default choices of the Maintenance Wizard. Other methods may be more appropriate in a production environment than the one shown here. They are discussed in "Choosing a Data Maintenance Method".

To load data into the dimensions: 

1. In the navigation tree, right-click the Dimensions folder or the folder for a particular dimension.

2. Choose Maintain Dimension.

   The Maintenance Wizard opens on the Select Objects page.

3. Select one or more dimensions from Available Target Objects and use the shuttle buttons to move them to Selected Target Objects.

4. Click Finish to load the dimension values immediately.

   The additional pages of the wizard enable you to create a SQL script or submit the load to the Oracle job queue. To use these options, click Next instead.

5. Review the build log, which appears when the build is complete. If the log shows that errors occurred, then fix them and run the Maintenance Wizard again.

   Errors are typically caused by problems in the mapping. Check for incomplete mappings or changes to the source objects.

Figure 3-8 shows the first page of the Maintenance Wizard. Only the Product dimension has been selected for maintenance. All the Product dimension members and attributes are fetched from the mapped relational sources.

Figure 3-8 Loading Dimension Values into the Product Dimension

Description of "Figure 3-8 Loading Dimension Values into the Product Dimension"

Figure 3-9 shows the Maintenance log for a dimension displayed by Analytic Workspace Manager. It refreshes throughout the build to provide you with the most up-to-date information.

Figure 3-9 Maintenance Log for the Product Dimension

Description of "Figure 3-9 Maintenance Log for the Product Dimension"

Displaying the Dimension View

The Maintenance Wizard automatically generates relational views of dimensions and hierarchies. Chapter 4 describes these views and explains how to query them.

Figure 3-10 shows the description of the relational view of the Product Primary hierarchy. You can view the data on the Data tab.

Figure 3-10 Product Primary Hierarchy View

Description of "Figure 3-10 Product Primary Hierarchy View"

Displaying the Default Hierarchy

After loading a dimension, you can display the default hierarchy.

To display the default hierarchy: 

1. In the navigation tree, right-click the name of a dimension.

2. Choose View Data.

Figure 3-11 shows the Primary hierarchy of the Product dimension.

Figure 3-11 Displaying the Product Primary Hierarchy

Description of "Figure 3-11 Displaying the Product Primary Hierarchy"

Creating Measures

Measures store the facts collected about your business. Each measure belongs to a particular cube, and thus shares particular characteristics with other measures in the cube, such as the same dimensions. The default characteristics of a measure are inherited from the cube.

To create a measure: 

1. Expand the folder for the cube that has the dimensions of the measure.

2. Right-click Measures, then choose Create Measure.

   The Create Measure dialog box is displayed.

3. On the General tab, enter a name for the measure.

4. Click Create.

   The measure appears in the navigation tree as an item in the Measures folder.

Figure 3-13 shows the General tab of the Create Measure dialog box.

Figure 3-13 Creating the Sales Measure

Description of "Figure 3-13 Creating the Sales Measure"

Mapping Cubes

You use the same interface to map cubes as you did to map dimensions, as described in "Mapping Dimensions". You can map a cube directly to a single fact table, or you can create more complex mappings using the OLAP expression syntax, which supports expressions, join conditions, and filters.

Although the dimension columns in a fact table typically contain only key values at the detail level, you can also map cubes to summary tables that contain the values from multiple levels. For example, a Time column might contain days, months, quarters, and years; a Geography column might contain cities, states, and countries. When a build rolls up the data in the cube from the detail level, the calculated values overwrite the loaded summary values, thereby correcting any inconsistencies.

Data Types

You can map cubes and measures to columns having these SQL data types:

• NUMBER

• INTEGER

• DECIMAL

• BINARY_FLOAT

• BINARY_DOUBLE

• VARCHAR2

• NVARCHAR2

• CHAR

• NCHAR

• DATE

• TIMESTAMP

• TIMESTAMP WITH TIMEZONE

• TIMESTAMP WITH LOCAL TIMEZONE

• INTERVAL YEAR TO MONTH

• INTERVAL DAY TO SECOND

Expressions

You can use the OLAP expression syntax when mapping cubes in the tabular view. This capability enables you to perform tasks like these as part of data maintenance, without any intermediate staging of the data:

• Perform calculations on the relational data using any combination of functions and operators available in the OLAP expression syntax.

• Create measures that are more aggregate than their relational sources. For example, suppose the Time dimension has columns for Day, Month, Quarter, and Year, and the fact table for Sales is related to Time by the Day foreign key column. In a basic mapping, you would store data in the cube at the Day level. However, you could aggregate it to the Month level during the data refresh. Using a technique called one-up mapping, you would map the cube to the Month column for Time, and specify a join between the dimension table and the fact table on the Day columns.

Join Conditions

In the tabular view, the mapping for each dimension includes a join condition. In the basic case where you are mapping the foreign keys in a fact table to the primary keys in the related dimension tables, you can leave the join condition blank. Analytic Workspace Manager derives this information from the relational source tables when you save the mapping.

For example, Analytic Workspace Manager provides this join condition for the TIME dimension in the UNITS_CUBE mapping:

GLOBAL.TIME_DIM.MONTH_ID = GLOBAL.UNITS_FACT.MONTH_ID

Filters

A filter applies a WHERE clause to the query that loads data from the relational source into the cube. You can use a filter to limit the rows to those matching a certain condition. This filter restricts the data to the year 2007:

GLOBAL.UNITS_FACT.MONTH_ID LIKE '2007%'

You can also use a filter to join two or more tables containing the measures. This filter joins the UNITS_FACT and PRICE_FACT tables in the Global schema on the Time (MONTH_ID) and Product (ITEM_ID) dimensions:

GLOBAL.PRICE_FACT.MONTH_ID=GLOBAL.UNITS_FACT.MONTH_ID AND GLOBAL.PRICE_FACT.ITEM_ID=GLOBAL.UNITS_FACT.ITEM_ID 

To map a cube: 

1. In the navigation tree, expand the cube folder and click Mappings.

   The Mapping window contains a schema navigation tree on the left and a mapping table for the cube and its dimensions. This is the tabular view.

2. To enlarge the Mapping window, drag the divider to the left.

3. In the schema tree, expand the tables, views, or synonyms that contain the data for the measures.

4. Drag-and-drop the source columns onto the appropriate cells in the mapping table for the cube.

5. After you have mapped all dimensions and measures, click Apply.

6. Drag the divider back to the right to reduce the size of the Mapping window.

Figure 3-14 shows the mapping canvas with the Units cube mapped to columns in the UNITS_FACT table. After you save the mappings, Analytic Workspace Manager provides the join conditions for base-level mappings such as the ones shown here.

Figure 3-14 Units Cube Mapped in the Tabular View

Description of "Figure 3-14 Units Cube Mapped in the Tabular View"

To calculate the facts of a measure as they are loaded into a cube: 

1. Create the cube.

2. Map all dimensions and measures to the source tables.

3. Edit the mapping of the measure to include a calculation in the OLAP expression syntax.

   For example, you might change UNITS_FACT.SALES to UNITS_FACT.SALES*1.06.

   You can use row expressions, column expressions, and conditions, but not nested SQL queries.

To map a cube above the detail level: 

1. Create the cube dimensions with the desired levels and map them to the source dimension table.

2. Create the cube and its measures.

3. Map each measure to its source column in the fact table.

4. For dimensions that are not being consolidated, map the detail level to its source column in the fact table, the same as you would in a basic cube mapping.

5. For dimensions being consolidated:

   1. Map the dimension to the appropriate column in the dimension table, not to the fact table. In the previous scenario, you would map the Month level of the Time dimension to the Month column of the Time dimension table. For example, you would map Month to time_dim.month_column.

   2. Enter a join condition between the fact table and the dimension table at the detail level. For example, time_dim.day_key = fact_tbl.day_foreign_key.

To map measures to different tables: 

1. Create the cube dimensions with the desired levels and map them to the source dimension table.

2. Create the cube and its measures.

3. Map each measure to its source column in the appropriate table.

4. Map the detail level of the dimensions to its source column in each of the tables. When you drop the additional source column names, you are asked whether to add or replace the existing mapping. Choose Add.

UNIT_PRICE: GLOBAL.PRICE_FACT.UNIT_PRICE
UNITS_SOLD: GLOBAL.UNITS_FACT.UNITS
MONTH:      GLOBAL.PRICE_FACT.MONTH_ID
            GLOBAL.UNITS_FACT.MONTH_ID
PRODUCT:    GLOBAL.PRICE_FACT.ITEM_ID
            GLOBAL.UNITS_FACT.ITEM_ID

UNITS_SOLD: GLOBAL.AMERICA.UNITS
            GLOBAL.EMEA.UNITS
TIME:       GLOBAL.AMERICA.MONTH_ID
            GLOBAL.EMEA.MONTH_ID
CHANNEL:    GLOBAL.AMERICA.CHANNEL_ID
            GLOBAL.EMEA.CHANNEL_ID
CUSTOMER:   GLOBAL.AMERICA.SHIP_TO_ID
            GLOBAL.EMEA.SHIP_TO_ID
PRODUCT:    GLOBAL.AMERICA.ITEM_ID
            GLOBAL.EMEA.ITEM_ID

Choosing a Partitioning Strategy

Partitioning is a method of physically storing the measures in a cube. It improves the performance of large measures in the following ways:

• Improves scalability by keeping data structures small. Each partition functions like a smaller measure.

• Keeps the working set of data smaller both for queries and maintenance, since the relevant data is stored together.

• Enables parallel aggregation during data maintenance. Each partition can be aggregated by a separate process.

• Simplifies removal of old data from storage. Old partitions can be dropped, and new partitions can be added.

The number of partitions affects the database resources that can be allocated to loading and aggregating the data in a cube. Partitions can be aggregated simultaneously when sufficient resources have been allocated.

The Cube Partitioning Advisor analyzes the source tables and develops a partitioning strategy. You can accept the recommendations of the Cube Partitioning Advisor, or you can make your own decisions about partitioning.

Choosing a Dimension for Partitioning

If your partitioning strategy is driven primarily by life-cycle management considerations, then you should partition the cube on the Time dimension. Old time periods can then be dropped as a unit, and new time periods added as a new partition. In Figure 3-16, for instance, the Quarter level of the Time dimension is used as the partitioning key. The Cube Partitioning Advisor has a Time option, which recommends a hierarchy and a level in the Time dimension for partitioning.

If life-cycle management is not a primary consideration, then run the Cube Partitioning Advisor and choose the Statistics option. The Cube Partitioning Advisor develops a strategy designed to achieve optimal build and query performance.

To run the Cube Partitioning Advisor: 

1. Map the cube to its data source, if you have not done so already.

2. On the navigation tree, select the cube to display its property pages.

3. On the Partitioning tab, click Cube Partitioning Advisor.

4. Choose Partition Using a Time Dimension or Partition Using Statistics.

   Wait while the Cube Partitioning Advisor analyzes the cube. When it is done, the Cube Partitioning Advisor displays its recommendations.

5. Evaluate the recommendations of the Cube Partitioning Advisor.

   • Select Accept Partition Advice to accept the recommendations. The cube is re-created with the partitions.

   • Clear the Accept Partition Advice box to reject the recommendations.

6. Click OK.

Figure 3-15 shows the Cube Partitioning Advisor dialog box.

Figure 3-15 Partitioning a Cube

Description of "Figure 3-15 Partitioning a Cube"

Example of a Partitioned Dimension

The Cube Partitioning Advisor might recommend partitioning at the Quarter level of the Calendar hierarchy of the Time dimension. Each Quarter and its descendants are stored in a separate partition. If there are three years of data in the analytic workspace, then partitioning on Quarter produces 12 bottom partitions, in addition to the default top partition. The top partition contains all remaining levels, that is, those above Quarter (such as Year) and those in other hierarchies (such as Fiscal Year or Year-to-Date).

Figure 3-16 illustrates a Time dimension partitioned by Quarter.

Figure 3-16 Partitioning Time by Quarter

Description of "Figure 3-16 Partitioning Time by Quarter"

Loading Data Into Cubes

You load data into cubes using the same methods as dimensions. However, loading and aggregating the data for your business measures typically takes more time to complete. Unless you are developing a dimensional model using a small sample of data, you may prefer to run the build in one or more background processes.

To load data into a cube: 

1. In the navigation tree, right-click the Cubes folder or the name of a particular cube.

2. Choose Maintain Cube.

   The Maintenance Wizard opens on the Select Objects page.

3. Select one or more cubes from Available Target Objects and use the shuttle buttons to move them to Selected Target Objects. If the dimensions are loaded, you can omit them from Selected Target Objects.

4. On the Dimension Data Processing Options page, you can keep the default values.

5. On the Task Processing Options page, you can submit the build to the Oracle job queue or create a SQL script that you can run outside of Analytic Workspace Manager.

   You can also select the number of processes to dedicate to this build. The number of parallel processes is limited by the smallest of these numbers: the number of partitions in the cube, the number of processes dedicated to the build, and the setting of the JOB_QUEUE_PROCESSES initialization parameter.

   Click Help for information about these choices.

6. Click Finish.

Figure 3-17 shows the build submitted immediately to the Oracle job queue.

Figure 3-17 Selecting the Scheduling Options

Description of "Figure 3-17 Selecting the Scheduling Options"

Example 3-2, "Maintenance Log for the Units Cube" shows the maintenance log displayed by Analytic Workspace Manager for a cube. The log refreshes throughout the build to provide you with the most up-to-date information. The maintenance log is displayed automatically for maintenance tasks that run immediately in the session. When you submit a job to the Oracle job queue, you can track its progress through the various reports in the Maintenance Reports folder: Jobs Scheduled, Jobs Running, and Jobs History. The reports in Jobs Running and Jobs History are the same as the one shown in Example 3-2.

Figure 3-18 Maintenance Log for the Units Cube

Description of "Figure 3-18 Maintenance Log for the Units Cube"

Displaying the Data in a Cube

After loading a cube, you can display the data for your business measures in Analytic Workspace Manager.

To display the data in a cube: 

1. In the navigation tree, right-click the cube.

2. Choose View Data from the shortcut menu.

The Measure Data Viewer displays the selected measure in a crosstab at the top of the page and a graph at the bottom of the page. On the crosstab, you can expand and collapse the dimension hierarchies that label the rows and columns. You can also change the location of a dimension by pivoting or swapping it. If you want, you can use multiple dimensions to label the columns and rows, by nesting one dimension under another.

To change the default display: 

• To pivot, drag a dimension from one location and drop it at another location, usually above or below another dimension.

• To swap dimensions, drag and drop one dimension directly over another dimension, so they exchange locations.

To make extensive changes to the selection of data, choose Query Builder from the File menu.

Figure 3-19 shows the Units cube in the Measure Viewer.

Figure 3-19 Displaying the Units Cube

Description of "Figure 3-19 Displaying the Units Cube"

Displaying the Cube View Descriptions

The Maintenance Wizard automatically generates relational views of a cube. Chapter 4 describes these views and explains how to query them.

Figure 3-20 shows the description of the relational view of the Units cube.

Figure 3-20 Description of the Units Cube View

Description of "Figure 3-20 Description of the Units Cube View"

Creating and Executing Custom Cube Scripts

A cube script is an ordered list of steps that prepare a cube for querying. Each step represents a particular data transformation. By specifying the order in which these steps are performed, you can allow for interdependencies.

You can choose from these step types:

• Clear Data: Clears the data from the entire cube, from selected measures, or from selected portions of the cube. You can clear just the detail data (called leaves) for a fast refresh, just the aggregate data, or both for a complete refresh. Clearing old data values is typically done before loading new values.

• Load: Loads the data from the source tables into the cube. You can load all measures in the cube or just selected measures.

• Aggregation: Generates aggregate values using the rules defined for the cube. You can aggregate the entire cube, selected measures, or selected portions of the cube.

• Analyze: Generates optimizer statistics, which can improve the performance of some types of queries. For more information, see "Analyzing Cubes and Dimensions". Generating statistics is typically done immediately after data maintenance.

• OLAP DML: Executes a command or program in the OLAP DML.

• PL/SQL: Executes a PL/SQL command or script. You can run a PL/SQL script, for example, at the beginning of data maintenance to initiate a refresh of the relational source tables.

If a cube is used to support advanced analytics in a cube script, then it cannot be enhanced as a cube materialized view, as described in "Adding Materialized View Capability to a Cube". In this case, you are responsible for detecting when the data in the cube is stale and must be refreshed.

Creating Cube Scripts

To create a cube script:

1. Expand the folder for a cube that is not defined as a cube materialized view.

2. Right-click Cube Scripts, then choose Create Cube Script.

   The Create Cube Script dialog box is displayed.

3. On the General tab, enter a name for the cube script.

4. To create a step, click New Step.

5. Choose the type of step.

   The New Step dialog box is displayed for that type of step.

6. Complete the tabs, then click OK.

   The step is listed on the Cube Script General tab.

7. Click Create.

   The cube script appears as an item in the Cube Script folder.

8. To run the cube script:

   1. Right-click the cube script on the navigation tree, and choose Run Cube Script.

      The Maintenance Wizard opens.

   2. Follow the steps of the wizard.

   3. To view the results, right-click the cube and choose View Data.

Figure 3-21 shows the Create Cube Script dialog box, in which several steps have been defined.

Figure 3-21 Creating a Cube Script

Description of "Figure 3-21 Creating a Cube Script"

Creating and Executing Maintenance Scripts

A maintenance script is an ordered list of steps for maintaining multiple cubes in a schema. By using a maintenance script, you can manage interdependencies among the cubes.

To load and aggregate a cube or a dimension, add it as a step. For more control over the maintenance of a particular cube or dimension, either create a cube script or enter the individual steps directly into the maintenance script:

• Clear Data

• Load

• Aggregation

• Analyze

• OLAP DML

• PL/SQL

These are the same steps described in "Creating and Executing Custom Cube Scripts".

Creating Maintenance Scripts

To create a maintenance script:

1. In the navigation tree, right-click Maintenance Scripts, then choose Create Maintenance Script to display the Create Maintenance Script dialog box.

2. Enter the name, labels, and description on the General tab.

3. To create a new step, click Add, then select the type of step from the list.

4. Create additional steps as desired. You can edit, delete, or re-order the steps at any time.

5. Click Create. The new maintenance script appears as an object in the Maintenance Scripts folder.

Figure 3-22 shows the General tab of the Create Maintenance Script dialog box.

Figure 3-22 Creating a Maintenance Script

Description of "Figure 3-22 Creating a Maintenance Script"

Adding Materialized View Capability to a Cube

Oracle OLAP cubes can be enhanced with materialized view capabilities. Cubes can be incrementally refreshed through the Oracle Database materialized view subsystem, and they can serve as targets for transparent rewrite of queries against the source tables. A cube that has been enhanced in this way is called a cube materialized view.

The OLAP dimensions associated with a cube materialized view are also defined with materialized view capabilities.

A cube must conform to these requirements, before it can be designated as a cube materialized view:

• All dimensions of the cube have at least one level and one level-based hierarchy. Ragged and skip-level hierarchies are not supported. The dimensions must be mapped.

• All dimensions of the cube use the same aggregation operator, which is either SUM, MIN, or MAX.

• The cube has one or more dimensions and one or more measures.

• The cube is fully defined and mapped. For example, if the cube has five measures, then all five are mapped to the source tables.

• The data type of the cube is NUMBER, VARCHAR2, NVARCHAR2, or DATE.

• The source detail tables support dimension and rely constraints. If they have not been defined, then use the Relational Schema Advisor to generate a script that defines them on the detail tables.

• The cube is compressed.

• The cube can be enriched with calculated measures, but it cannot support more advanced analytics in a cube script.

To add materialized view capabilities: 

1. In the navigation tree, select a cube.

   The property sheets for the cube are displayed.

2. Choose the Materialized Views tab.

3. Review the checklist and, if some tests failed, fix the cause of the problem.

   You cannot define a cube materialized view until the cube is valid.

4. For automatic refresh, complete just the top half page. For query rewrite, complete the entire page.

   Click Help for information about the choices on this page.

5. Click Apply.

The cube materialized views appear in the same schema as the analytic workspace. A materialized view is created for the cube and each of its dimensions. Unlike traditional materialized views, cube materialized views do not use relational tables to store data; the data is stored in the backing cube. A CB$ prefix identifies the tables as cube materialized views.

The initial state of a new materialized view is invalid, so it does not support query rewrite until after it is refreshed. You can specify the first refresh time on the Materialized View tab of the cube, or you can run the Maintenance Wizard.

Figure 3-23 shows the Materialized View tab of the Units Cube.

Figure 3-23 Defining a Materialized View

Description of "Figure 3-23 Defining a Materialized View"