5Creating Analyses
Creating Analyses
Creating Analyses
What are Analyses
An analysis is a query against an organization's data that provides answers to business questions. A query contains the underlying SQL statements that are issued to the Oracle BI Server.
Analyses let you explore and interact with information by visually presenting data in tables, graphs, pivot tables, and so on. You can save, organize, and share the results of analyses.
Analyses that you create can be saved in the Oracle BI Presentation Catalog and integrated into any Oracle BI EE dashboard. Analyses can be enhanced through features such as graphs, result layout, calculated items, and drilling.
How are Analyses Processed
When an analysis is processed, the request is sent in the form of logical SQL statements to the Oracle BI Server. The BI Server generates queries against the appropriate data sources. When the BI Server receives the query results, they are in a format that is not yet suitable for returning to the user. The BI Server merges the results and applies any additional calculations or filters that pertain to the results. The BI Server then sends the data to Oracle BI Presentation Services to format the results for display.
Embedding an Analysis in a Dashboard
This is a short description.
Embedding an analysis in a dashboard causes it to execute automatically and display the most current results every time the dashboard is accessed. This provides access to the most current results. For example, if you are a recruiter, then you might want to have metrics on your currently open requisitions displayed on the front page of your dashboard.
You can embed saved analyses by using the Dashboard editor. For information about the Dashboard editor, see "Dashboard Content".
Subject Areas and Columns
What are Subject Areas and Columns
A subject area contains folders, measure columns, attribute columns, hierarchical columns, and hierarchy levels that represent information about the areas of an organization's business or about groups of users with an organization. Subject areas usually have names that correspond to the types of information that they contain, such as Recruiting, Onboarding, and Performance Management.
A subject area corresponds to the presentation layer in an Oracle BI metadata repository. In a repository, the subject area is the highest-level object in the presentation layer and represents the view of the data that end users see when they create or edit an analyses.
Individuals who design and build metadata repositories (Oracle Administrators) create subject areas using the Oracle BI Administration Tool. Generally, rather than creating one large subject area for their company's data, they create multiple smaller subject areas. This enables them to provide a particular group of users or a particular area of a company with the most important data that they need in one small subject area and the less important data in one or more related subject areas created from the same business model layer. Having these smaller subject areas makes it easier for users to find the data they need. It also makes it easier to maintain the data.
The individuals who design and build metadata repositories can specify that a subject area, folder (and its children), or column (both attribute and hierarchical) is to be hidden. A hidden subject area, folder, or column is not visible in the"Subject Areas pane" but is visible elsewhere, such as in an analysis or saved filter contents. (Because the object is still visible elsewhere, hiding a subject area, folder, or column in this way is not a solution for security or access control.)
If the criteria of an existing analysis includes a subject area, folder, or column that is subsequently hidden, the analysis is still accessible but the subject area, folder, or column is no longer visible in the Subject Areas pane of the "Analysis editor: Criteria tab".
Columns contain the individual pieces of data that an analysis returns. Columns usually have names that indicate the types of information that they contain, such as Account or Contact. Together with filters and selection steps, columns determine what data an analysis contains.
Typically, when you query a single subject area, all the measure columns that are exposed in that subject area are compatible with all the attribute columns and hierarchical columns that are exposed in the same subject area.
For an analysis to return data, you must select at least one column to include in the analysis.
What are the Types of Columns
Attribute Column
Holds a flat list of values that are also known as members. No hierarchical relationship exists between these members, as is the case for members of a hierarchical column.
Examples include Requisition ID or Name
Hierarchical Column
Holds data values that are organized using both named levels and parent-child relationships. This column is displayed using a tree-like structure. Individual members are shown in an outline manner. Hierarchies allow you to drill deeper into the data, to view more detailed information. Examples include Organization or Location.
A hierarchical column can be one of the following kinds:
Level-based hierarchy: Consists of an ordered set of two or more levels. For example, a Time hierarchy might have three levels for Year, Quarter, and Month. Level-based hierarchies can also contain parent-child relationships.
Parent-child hierarchy: Consists of values that define the hierarchy in a parent-child relationship and does not contain named levels. For example, an Employee hierarchy might have no levels, but instead have names of employees who are managed by other employees. Employees can have titles, such as Vice President. Vice Presidents might report to other Vice Presidents and different Vice Presidents can be at different depths in the hierarchy.
In addition to being level-based or parent-child, hierarchical columns can be one of the following:
Ragged: A hierarchy in which all the lowest-level members do not have the same depth. For example, a Time hierarchy might have data for the current month at the day level, the previous month's data at the month level, and the previous 5 years' data at the quarter level. This type of hierarchy is also known as an unbalanced hierarchy.
Skip-level: A hierarchy in which certain members do not have values for certain higher levels. For example, in the United States, the city of Washington in the District of Columbia does not belong to a state. The expectation is that users can still navigate from the country level (United States) to Washington and below without the need for a state.
Measure Column: Holds a simple list of data values. It is a column in an Oracle BI EE repository, usually in a fact table, that can change for each record and can be added up or aggregated in some way. Examples include Revenue or Units Sold.
Throughout this guide, the term "column" on its own generally refers to all three types. Names for specific types of columns are included where necessary.
How are Columns Indicated Visually
Each type of column is indicated by its own icon in places such as the Subject Areas pane and Layout pane. You can expand level-based hierarchies and see their levels. Parent-child hierarchies are shown as hierarchical columns that have no levels.
Understanding the Double Column Feature
Oracle BI EE provides a feature called double columns. When a repository is configured for the double column feature, column data includes a display column that has a code column mapped to it (that is, it has double columns). A display column contains the display values for the column data, for example, Excellent, Good, and Poor. A code column contains code values that uniquely identify display values and are consistent across users or locales, for example, the code values 1 (uniquely identifying Excellent), 2 (uniquely identifying Good), and 3 (uniquely identifying Poor).
When Oracle BI EE processes a double column, for example, as part of the criteria of an analysis or as the basis for a filter, it generates and issues SQL statements to the Oracle BI Server that use code values rather than display values, thereby making the filter language independent.
The double column feature lets you:
Build internationalized analyses that are automatically filtered by language independent codes. For example, you can build one filtered analysis that can be used by both French-language users and English-language users.
Build common analyses that can be shared among groups even if the groups require different display values. For example, you can build a common analysis that displays for a Status field the values Excellent, Good, and Poor to one group, and Superb, Okay, and Bad to another group.
Change display values without breaking existing analyses. For example, suppose that you have an analysis that is filtered on the display value Excellent. If it is a double column, then the filtering is performed on its code column with the code value for "Excellent" rather than the display value of "Excellent." This means that you can change the display value Excellent to Superb without breaking the existing analysis.
In addition, if your organization allows the display of code values within Oracle BI EE, you can use code values rather than the display values in some dialogs, such as the "New Filter dialog". This means, for example, when specifying the values to use when applying a filter, you can specify code values rather than display values.
Before you can take advantage of the double column feature, your administrator must configure your repository by mapping code columns to display columns. Check with the administrator to see if your repository is configured for double columns.
What is the Analysis Editor
The "Analysis editor" lets you explore and interact with information by visually presenting data in tables, graphs, pivot tables, and so on. You can include the views that you create in an analysis for display in dashboards.
The Analysis editor contains the following tabs:
Criteria tab — Lets you specify the criteria for an analysis, including columns, and filters. You can specify the order in which the results should be returned, formatting (such as headings, number of decimal places, styles such as fonts and colors, and conditional formatting), and column formulas (such as adding a Rank or Percentile function).
Results tab — Lets you create different views of the analysis results such as graphs, tickers, and pivot tables. You can also add or modify selection steps.
Prompts tab — Lets you create prompts that allow users to select values to filter an analysis or analyses on a dashboard. Prompts allow users to select values that dynamically filter all views within the analysis or analyses. You can also create prompts for use with selection steps, both for member selection steps and qualifying condition steps.
Advanced tab — Lets you edit XML code and examine the logical SQL statement that was generated for an analysis. You can use the existing SQL statement as the basis for creating a new analysis.
The tabs of the Analysis editor are organized into various panes and areas. As you work, you can manage these panes to suit your needs. See"Managing Panes in the Analysis Editor Tabs".
You access the Analysis editor when you create (or edit) an analysis. See "Creating New Analyses".
Managing Panes in the Analysis Editor Tabs
This is a short description.
Each of the tabs in the Analysis editor consists of several panes. For example, the Results tab consists of the "Subject Areas pane", the "Catalog pane", the "Views pane", the "Compound Layout", and the "Selection Steps pane".
As you work, you can manage these panes to give yourself the most efficient work area for the task that you are performing. For example, if you add multiple views to the compound layout on the Results tab, then you might want to hide the other panes in the Results tab to maximize the area in which to see the views that you are adding. You can:
Show or hide the panes that are displayed on the left side of the Analysis editor by clicking the sideways triangle button that is displayed between the left and right sides.
Expand or collapse an individual pane by clicking the plus or minus button in the title bar of the pane.
Resize an expanded pane by dragging its splitter. For example, you can drag the splitter on top of the Catalog pane up to increase the height of the pane.
Show or hide a pane by clicking its show/hide toolbar button, if one is available for the pane on the toolbar. For example, you can show or hide the Filters pane in the Criteria tab by clicking the Show/Hide Filters pane button on the toolbar.
What is the Process for Constructing Analyses
The process for constructing an analysis includes the following steps:
Creating New Analyses
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This procedure is a step in the process for constructing an analysis. For more information, see "What Is the Process for Constructing Analyses?"
Select a subject area.
The Analysis editor is displayed.
Specify the criteria for the analysis. See "Specifying the Criteria for Analyses".
Specifying the Criteria for Analyses
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This procedure is a step in the process for constructing an analysis. For more information, see "What Is the Process for Constructing Analyses?"
You use the "Analysis editor: Criteria tab" to specify the criteria for an analysis, including columns, filters, and selection steps. You also can specify:
The default order in which columns are to be displayed in the analysis results
The default sorting for the values that are displayed in views
The default formatting (such as headings, number of decimal places, conditional formats, and system wide defaults)
Column formulas (such as adding a Rank or Percentile function)
The aggregation rules for column totals
You can now add views to the analysis. See "Displaying the Results of Analyses".
Column Formulas
You can edit the formulas for attribute columns and measure columns when you specify the criteria for an analysis. This editing affects the column only in the context of the analysis and does not modify the formula of the original column in the subject area. You can also customize table and column headings and specify the aggregation rule for column totals. (This functionality is not available for hierarchical columns.)
A column formula specifies what the column values represent. In its most basic form, such as "Base Facts"."1-Revenue", a column takes the data from the data source as is. You can edit the formula to add functions, conditional expressions, and so on. This editing enables you to present analysis results in a variety of ways. For example, suppose that you want to perform what-if analysis and show what the revenue might be by product if you increased revenue by 10%. You can see this increase by changing the formula for the Revenue column to show the revenue increased by 10%.
Related Topics
Editing the Formula of a Column
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Navigate to the analysis.
Edit the analysis.
In the "Selected Columns pane", click the Options button beside the column whose formula you want to edit and select Edit Formula. The "Edit Column Formula dialog" is displayed.
Use the "Edit Column Formula dialog: Column Formula tab" to perform various tasks such as creating customized headers and creating or editing the formula for the column. You can build a simple mathematical formula using operator and character buttons, such as "Base Facts"."1-Revenue"*1.10. You can click the Variable button to create and use a global variable to maintain a value that may need to be changed during the course of business across multiple analyses. You create a global variable as part of the process of creating an analysis. For example, you can create a global variable for the start of the fiscal year and change it as necessary for the start of each new fiscal year.
Optionally, use the "Edit Column Formula dialog: Bins tab" to combine values for the column into sets.
Click OK.
The column formula is saved with the analysis in which it is used.
Combining Columns Using Set Operations
After you have selected a subject area for an analysis, you can combine columns from one or more subject areas using Set operations such as Union or Intersect. By combining columns, you create a new column for displaying the data in a different way.
Guidelines for Selecting Columns to Combine
When selecting columns to combine, keep the following guidelines in mind:
The number and data types of the columns to combine must be the same. The number of rows that are returned for each column can differ.
You can select columns from the same subject area or from a different subject area, but the columns must have some commonality.
You can specify one Set operation for one collection of criteria. For example, if you create criteria from the A-Sample Sales subject area, you can apply only one Set operation to those columns. You cannot apply different Set operations to different columns in the collection of criteria.
You cannot use hierarchical columns, selection steps, or groups when you combine criteria.
Difference Between Combining Columns Using Set Operations and Adding Columns from Related Subject Areas
Combining columns using Set operations produces different results than adding columns from related subject areas:
When you combine columns using Set operations, the analysis results show a single newly combined column governed by a Set operation. For example, see "Combining Columns from One or More Subject Areas".
When you add columns from related subject areas to an analysis, the results show each added column individually. For example, if you have the appropriate permissions, then you can create an analysis by selecting one column from a primary subject area and selecting another column from a related subject area.
For information, see "What Are Subject Areas and Columns?"
Combining Columns from One Subject Area
The A-Sample Sales subject area contains the Offices folder, which contains the D1 Office and D2 Department columns. You can combine these two columns and create a new column called Offices & Departments. You can include the Union All Set operation to specify that this new column shows all the values from both columns in a single column in a table.
Combining Columns from one or more Subject Areas
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Analysis Results
This is a short description.
You use the "Analysis editor: Results tab" to add different views of the results such as graphs, tickers, and pivot tables. For a list of the views that you can add, see "What Types of Views Are Available?"
When you create an analysis and display the Results tab, you see in the "Compound Layout" a title view and either a table or pivot table view by default, based on the following:
If the analysis contains only attribute columns and measure columns, then a table is displayed by default.
If the analysis contains at least one hierarchical column, then a pivot table is displayed by default.
You can combine views and position them anywhere in the Compound Layout. For example, you can create side-by-side pivot tables that reflect different views of the data, graphs that allow you to explore interrelationships in depth, and filters that limit the results.
Together all of these views are called a compound layout. You can create additional compound layouts, as described in "Creating Additional Compound Layouts".
Displaying the Results of Analyses
This procedure is a step in the process for constructing an analysis. For more information, see "What Is the Process for Constructing Analyses?"
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Click the Results tab.
(Optional) Edit the table or pivot table or add additional views. For more information, see "Adding Views for Display in Dashboards".
The results of the analysis are displayed in a table or pivot table.
Additional Compound Layouts
As you work with an analysis, you can create multiple compound layouts that use different combinations of views. For example, one layout might include a graph and a title, and another layout might include a graph and a narrative view. When you add the analysis to a dashboard page, you can select which layout you want to include on that page.
Creating Additional Compound Layouts
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Edit the analysis.
Click the "Analysis editor: Results tab".
Create another compound layout in the "Compound Layout" by clicking one of the following buttons on the toolbar of the Results tab:
Create Compound Layout — Creates a new instance of the compound layout.
Duplicate Compound Layout — Creates a copy of the current compound layout, with the same views.
No Data in the Results Alert
This is a short description.
When the results of an analysis return no data, the following default message is displayed to users:
No Results The specified criteria didn't result in any data. This is often caused by applying filters and/or selections that are too restrictive or that contain incorrect values. Please check your Analysis Filters and try again. The filters currently being applied are shown below.
Rather than display the default message, you can create a customized message to alert users. This message enables you to provide your own explanation for why the analysis returned no data.
Alerting Users to No Data in the Results of Analyses
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Edit the analysis to which you want to add a custom message.
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Click the "Analysis editor: Results tab".
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Click the Analysis Properties toolbar button. The "Analysis Properties dialog" is displayed.
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In the No Results Settings box, select Display Custom Message.
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In the Header field, enter the text of the header for the custom message.
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In the Message field, enter the explanatory text.
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Click OK.
Adding Prompts to Analyses
This procedure is a step in the process for constructing an analysis. For more information, see "What Is the Process for Constructing Analyses?"
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Edit the analysis to which you want to add a prompt.
Click the Prompts tab.
Add the prompt, as described in Chapter 6, "Prompting in Dashboards and Analyses".
Examining the Logical SQL Statements for Analyses
You use the "Analysis editor: Advanced tab" to examine the XML code and logical SQL statement that is generated for an analysis and optionally create a new analysis based on that SQL statement. Generally you need not use the features of this tab, because the vast majority of functionality for working with analyses is provided through the user interface. The following list provides examples of situations in which you might want to examine SQL statements:
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You can use the Prefix section to override any user variable or session variable that has been marked as available for updating. You can specify multiple variables to update with one line of code.
To override variables, use the following syntax:
SET VARIABLE var-name=valueFor example, you can set the variables that are shown in the following code line:
SET VARIABLE "My Variable"=4, DISABLE_CACTHE_HIT=1, LOGLEVEL = 2;For information on variables, see "Where Can I Reference Variables?"
You can temporarily change the logging level for an analysis for troubleshooting, even when logging is turned off for analyses. In the Prefix section of the Advanced tab, you can specify the following:
SET VARIABLE LOGLEVEL=4;This specification runs the analysis at level 4, which facilitates advanced troubleshooting. The default level is 0, which indicates no logging. You can view the logs using the View Log link on the "Administration: Manage Sessions page.
You can enter your own logical SQL statements. Entering your own statements eliminates many features of Oracle BI EE (such as hierarchical columns, selection steps, groups, and formatting), but enables you to use functions such as SET operations.
You can create a new analysis using the SQL statements from an existing analysis as your starting point.
Before working with the Advanced tab, keep the following important points in mind:
This procedure is only for advanced users and developers that have the appropriate responsibilities to access the Advanced tab. It is assumed that you understand advanced SQL statements, have expertise working with the Oracle BI Server metadata, are familiar with the SQL information in Appendix D, "Logical SQL Reference", and understand the content and structure of the underlying data sources.
This tab provides the Advanced SQL Clauses area, which enables you to add clauses to the SQL statements and other similarly advanced features. These features, except for the ability to specify a prefix, are disabled for analyses that contain hierarchical columns, member selections, or groups.
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The links at the top of the tab allow you to download analysis results into Microsoft Excel. You might be unable to download analysis results based on the security features that have been configured at your organization. For information, contact your administrator.Note: You can see the logical SQL statement that an analysis is using by inserting a logical SQL view. You can also enter SQL statements using the "Administration: Issue SQL page".
Editing XML Code or SQL Generated for an Analysis
This procedure is a step in the process for constructing an analysis. For more information, see "What Is the Process for Constructing Analyses?"
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Saving Analyses
You can save an analysis to a:
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Personal folder, from which only you can access it.
Your top-level personal folder is called My Folders. Every user with a unique user name has a folder called My Folders. This is designed to hold the analyses that you run most often, and other content that you access frequently.
When you click the Refresh Display link at the bottom of the pane, the analysis is listed under the folder in which you saved it.
Shared folder, from which users with permission to access that folder can access it.
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Lotus Notes database by either:
Saving the analysis to a Web archive file (file extension .mht), and then uploading the file to the Lotus Notes database.
Saving the analysis and scheduling it to be sent by email automatically to a specific address on a Lotus Notes server using an agent. The Lotus Notes server can then process the email and write it to the database.
Saving an Analysis
This procedure is a step in the process for constructing an analysis. For more information, see "What Is the Process for Constructing Analyses?"
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Edit the analysis.
In the Analysis editor, click the Save Analysis toolbar button to display the dialog to save the analysis.
If you want to save the analysis to a personal or shared folder:
In the Save In box, select the personal or shared folder in which to save the analysis.
In the Name field, enter a name for the analysis, such as hiring_manager_open_requisitions.
Click OK.
If you want to save the analysis to a Lotus Notes database using a Web Archive file:
In the Save In box, select the folder in which to save the analysis.
In the Name field, enter a name for the analysis, including an .mht file extension, such as hiring_manager_open_requisitions.mht.
Click OK.
Upload the .mht file to the Lotus Notes database.
f you want to save the analysis to a Lotus Notes database using an agent:
Creating Agents from Analyses
This is a short description.
You can create an agent directly from an analysis. When you create an agent using this method, Oracle BI EE does the following:
Creates a condition based on the analysis and adds the condition to the "Agent editor: Condition tab"
Adds the analysis as the content delivery on the "Agent editor: Delivery Content tab"
For more information on agents, see "Delivering Content".
Creating an Agent from an Analysis
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Locate the analysis from which you want to create an agent using one of the following methods:
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Open the analysis, click the "Analysis editor: Results tab" and then the Schedule toolbar button.
The analysis is saved and then the "Agent editor" is displayed.
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Navigate to the analysis on the "Catalog page", click the More list, and then click the Schedule option.
The "Agent editor" is displayed.
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Complete the following tabs of the Agent editor:
Save the agent.
Editing an Analysis
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In the global header, click Catalog to display the "Catalog page".
Navigate to the analysis to edit and click the Edit link. The analysis is displayed in the "Analysis editor".
Make the desired changes.
Save the analysis.
About Embedding an Analysis in a Dashboard
Embedding an analysis in a dashboard causes it to execute automatically and display the results within the dashboard. This provides access to current results. For example, if you are a sales executive whose company captures sales data on a daily basis, then you might want to have the dollar volume of product that was sold today be displayed on the front page of your dashboard.
You can embed previously created analyses from the Oracle BI Presentation Catalog by using the Dashboard editor. For information about the Dashboard editor, see "Adding Content to a Dashboard".
Using Variables
You can reference variables in several areas of Oracle BI, including in analyses, dashboards, KPIs, actions, agents, and conditions. For example, suppose that you wanted to create an analysis whose title displays the current user's name. You can do this by referencing a variable.
Presentation Variables
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Column prompt — A presentation variable created as part of a column prompt is associated with a column, and the values that it can take come from the column values.
To create a presentation variable as part of a column prompt, in the "New Prompt dialog" (or Edit Prompt dialog), you have to select Presentation Variable in the Set a variable field and then enter a name for the variable in the Variable Name field.
For information on working with column prompts, see "Creating a Column Prompt".
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Variable prompt — A presentation variable created as part of a variable prompt is not associated with any column, and you define the values that it can take.
To create a presentation variable as part of a variable prompt, in the "New Prompt dialog" (or Edit Prompt dialog), you have to select Presentation Variable in the Prompt for field and then enter a name for the variable in the Variable Name field.
For information on working with variable prompts, see "Creating a Variable Prompt".
The value of a presentation variable is populated by the column or variable prompt with which it was created. That is, each time a user selects a value in the column or variable prompt, the value of the presentation variable is set to the value that the user selects.
Global Variables
You can create and use a global variable to maintain a value that may need to be changed during the course of business across multiple analyses.
You create a global variable as part of the process of creating an analysis. For example, you can create a global variable for the start of the fiscal year and change it as necessary for the start of each new fiscal year.
Global variables can be of the following types:
Date
Date and Time
Number
Text
Time
Where Can I Reference Variables
You can reference variables in the following areas (but not all types of variables can be referenced in each area):
Title views. See "Results tab: Title editor"
Narrative views. See "Results tab: Narrative editor"
Static text views. See "Results tab: Static Text editor"
Filters. See "New Filter dialog" and "New EVALUATE_PREDICATE Function dialog".
Column formulas. See "Edit Column Formula dialog: Column Formula tab"
Conditional formatting conditions. See "Column Properties dialog: Conditional Format tab" and "New Condition dialog".
Table and column headings in analyses. See "Column Properties dialog: Column Format tab"
Dashboard prompts and inline prompts. See "New Prompt dialog"
Headers and footers for PDF output. See "Edit Footer or Header dialog"
Link or image objects in a dashboard. See "Link or Image Properties dialog"
Text objects in a dashboard. See "Text Properties dialog"
Graphs to specify conditional formatting of graph data. See "Graph Properties dialog: Style tab" and "Style and Conditional Formatting dialog: Conditional Formatting tab"
Gauge thresholds. See "Enter Expression dialog"
Gauge limits. See "Customize Gauge Limit dialog"
Agents. See the "Agent editor: Delivery Content tab"
Actions to specify parameters. See "Create New Action dialog" and "Edit Parameter Mapping dialog"
Conditions to specify parameters. See "New Condition dialog"
Selection steps. See "New Condition Step dialog"
KPIs to define thresholds. See "KPI editor: States page"
KPIs included in a KPI watchlist. See "Add KPI dialog"
URL fields in dialogs. See "Image Map Prompt Properties dialog"
For the syntax that you use to reference variables, see "What Is the Syntax for Referencing Variables?"
What is the Syntax for Referencing Variables
You can reference variables in analyses, dashboards, KPIs, and agents. How you reference a variable depends on the task that you are performing.
For tasks where you are presented with fields in a dialog, you must specify only the type and name of the variable (not the full syntax), for example, referencing a variable in a filter definition.
For other tasks, such as referencing a variable in a title view, you specify the variable syntax. The syntax you use depends on the type of variable as described in the Syntax for Referencing Variables table.
Syntax for Referencing Variables Table
| Type of Variable | Syntax | Example |
|---|---|---|
| Presentation |
@{variables.variablename}[format]{defaultvalue} or @{scope.variables['variablename']}
where:
|
@{variables.MyFavoriteRegion}{EASTERN REGION} or @{dashboard.variables['MyFavoriteRegion']} |
You also can reference variables in expressions. The guidelines for referenceing vairables in expressions are described in the Guidelines for Referencing Variables in Expressions table.
Guidelines for Referencing Variables in Expressions
| Type of Variable | Guidelines | Example |
|---|---|---|
| Presentation |
Note: If the @ sign is not followed by a {, then it is treated as an @ sign.
|
"Market"."Region"=@{MyFavoriteRegion}{EASTERN REGION} |
For the specific areas where you can reference variables, see "Where Can I Reference Variables?".
What Predefined Presentation Variables are Available
The table Predefined Presentation Variables contains a list of the predefined presentation variables that you can reference in analyses, dashboards, KPIs, and agents. (Note that the syntax for these predefined presentation variables omits the variables. qualifier, for example, @{session.locale} rather than @{session.variables.locale}.)
Predefined Presentation Variables
| Scope | Presentation Variable | Example |
|---|---|---|
| system | productVersion | system.productVersion=11.1.1.3 (Build 090619.0110.000) |
| system | currentTime | system.currentTime=6/29/2009 7:35:59 PM |
| user | id | user.id=Administrator |
| user | displayName | user.displayName=Administrator |
| user | homeDirectory | u ser.homeDirectory=/users/administrator |
| dashboard | currentPage | dashboard.currentPage=page 1 |
| dashboard | xml | dashboard.xml=the dashboard XML |
| dashboard | dashboard.currency.name | dashboard.currency.name=Euro |
| dashboard | dashboard.currency.symbol | dashboard.currency.symbol=$ |
| dashboard | dashboard.currency.userPreference | dashboard.currency.userPreference=Global Currency 1 |
| dashboard | dashboard.path This returns the path in the catalog. |
dashboard.path=/users/administrator/_ portal/Sales |
| dashboard | dashboard.name | dashboard.name=MyDashboard |
| dashboard | dashboard.caption This returns the localized name of the dashboard. | dashboard.caption=Sales |
| dashboard | dashboard.location This returns the URL for the location. |
dashboard.location = Dashboard&PortalPath=/users/administrator /_portal |
| dashboard | dashboard.description | dashboard.description=Sales by region and district |
| dashboard | dashboard.author | dashboard.author=Administrator |
| dashboard.c urrentPage | dashboard.currentPage.name | dashboard.currentPage.name=Sales page 1 |
| dashboard.c urrentPage | dashboard.currentPage.path | dashboard.currentPage.path = /users/administrator/_portal/Sales/page 1 |
| dashboard. current Page | dashboard.currentPage.currency.name | dashboard.currentpage.currency. name=USD |
| dashboard.c urrent Page | dashboard.currentPage.currency.symbol | dashboard.currentPage.currency. symbol=USD |
| dashboard. current Page | dashboard.currentPage.currency.userPreference | dashboard.currentPage.currency.userPreferenc e=Global Currency 2 |
| analysis | report.currency.name | report.currency.name=$ English - United States |
| analysis | report.currency.symbol | report.currency.symbol=$ |
| analysis | report.currency.userPreference | report.currency.userPreference=Global Currency 2 |
Example of Referencing a Variable in a Title View
Suppose that you have created a dashboard prompt called Region, which is based on the Region column and which creates a presentation variable called MyFavoriteRegion.
Suppose also that you have created an analysis that shows Dollars by Region and District. You have also added a filter on the Region column that is set to Is Prompted so that you can prompt the user for a region using this Region prompt.
You can reference the MyFavoriteRegion variable in the Title view so that the user's selection in the Region prompt is displayed in the title of the analysis. To do so, you enter the variable syntax @{variables.MyFavoriteRegion} in the Title field in the Title editor.