2 Creating Analyses
Topics
This chapter includes the following sections:
What Is an Analysis?
An analysis is a query against an organization's data that provides answers to business questions.
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. You can enhance analyses through features such as graphs, result layout, calculated items, and drilling.
How Do I Work with an Analysis?
You use an editor to work with an analysis.
To work with an analysis, you use one of the following editors:
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The Analysis editor — A feature-rich editor that allows you to explore and interact with information by visually presenting data in tables, graphs, pivot tables, and so on. See What Is the Analysis Editor?
You have access to this editor if you have been granted the Access to Analysis privilege by the administrator.
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The BI Composer wizard — A simple-to-use wizard that allows you to create, edit, or view analyses without the complexities of the Analysis editor. See Using BI Composer to Work with Analyses.
You have access to this editor only if you have been granted the Access to BI Composer privilege by the administrator.
You specify which editor you want to use on the Preferences tab of the My Account dialog. However, if you have turned on accessibility mode either in the Sign In page or the Preferences tab of the My Account dialog, then the BI Composer wizard in accessibility mode is used as the analysis editor, regardless of your choice.
How Is an Analysis 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 then 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.
How Can I Always See the Most Current Results of an Analysis?
Embedding an analysis in a dashboard causes it to execute automatically and display the most current results every time the dashboard is accessed.
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 a saved analysis by using the Dashboard editor. See Adding Content to Dashboards.
What Are Subject Areas and Columns?
Subject areas and columns are the building blocks for an analysis.
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 Marketing Contracts, Service Requests, and Orders.
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 analysis.
A related subject area is an external data source that is connected to a primary subject area in an analysis.
Individuals who design and build metadata repositories (such as a Business Intelligence strategist, metadata provider, or Extract Transform Load (ETL) developer) 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. See Creating Subject Areas in Metadata Repository Builder's Guide for Oracle Business Intelligence Enterprise Edition.
Note:
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 include 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 Criteria tab Analysis editor.
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.
When you create an analysis, filter, or dashboard prompt, you first select the subject area with which you want to work. This is known as the primary subject area and is displayed in the Subject Areas pane. If, as you work, you find that you need more data, you can add additional subject areas that are related to the primary subject area that you have chosen. (You can add related subject areas only if they are available for the primary subject area and only if you have permission to access them.)
Note:
You can add your own data to analyses too. See Adding External Data to Analyses.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. However, when you combine columns from multiple subject areas, you must ensure that you do not include combinations of measure columns with attribute columns and hierarchical columns that are incompatible with one another.
For example, a measure column in one subject area might not be associated with the Project attribute column. If measure columns associated with the Project attribute column from another subject area are added to the analysis along with columns that are not associated with Project, then the query might fail to return results, or cause the BI Server error No fact table exists at the requested level of detail: XXXX.
For an analysis to return data, you must select at least one column to include in the analysis.
What Are the Types of Columns?
Subject areas contain the following types of columns:
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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. An attribute column was referred to as a presentation column in previous releases (prior to 11g).
Examples include Produc tID or City.
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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 Time or Geography. The illustration shows the Time folder and the Time and Fiscal Time hierarchies expanded in the Subject Areas pane.
A hierarchical column is one of the following kinds:
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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.
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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 are at different depths in the hierarchy.
In addition to being level-based or parent-child, hierarchical columns are one of the following:
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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.
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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.
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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 you can add up or aggregate 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. The illustration shows the icons and names of various columns.
Understanding the Double Column Feature
Double columns need to be configured before they can display code columns and display columns for data.
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:
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Build internationalized analyses that are automatically filtered by language independent codes. (In previous releases (prior to 11g), filters were not language independent.) For example, you can build one filtered analysis that you can use with both French-language users and English-language users.
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Build common analyses that you can share 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.
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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:
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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). Additionally, you can change the sort order of the subject area and subject area folders. See Changing the Sort Order of Subject Areas and Subject Area Folders.
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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.
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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.
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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.
Note:
If you are using Oracle BI EE in accessibility mode, then, when you create (or edit) an analysis, the Analysis editor is replaced by the BI Composer wizard. For more information on BI Composer, see Using BI Composer to Work with Analyses. See Accessibility Features.Managing Panes in the Analysis Editor Tabs
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:
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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.
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Expand or collapse an individual pane by clicking the plus or minus button in the title bar of the pane.
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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.
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Show or hide a pane by clicking its show/hide 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 Show/Hide Filters pane on the toolbar.
What Is the Process for Constructing Analyses?
Creating useful analyses requires several steps.
Note:
If you are using Oracle BI EE in accessibility mode or you have selected Wizard (limited functionality) as the analysis editor, then you use the BI Composer wizard rather than the Analysis editor to construct analyses. See Using BI Composer to Work with Analyses and Accessibility Features.
The process for constructing an analysis includes the following steps:
Creating New Analyses
This is one step in the process for constructing an analysis.
See What Is the Process for Constructing Analyses?
Creating an analysis allows you to add that analysis to dashboards.
- In the global header, click New, then Analysis, then one of the following options:
- Select a subject area. If you purchased Oracle Business Intelligence Visual Analyzer, you can also select an external data source (uploaded Microsoft Excel file).
The Analysis editor is displayed. You can now specify the criteria for the analysis.
- Create Direct Database Query
- Create Analysis from Simple Logical SQL
The Analysis Simple SQL Statement dialog is displayed in which you enter SQL statements for creating an analysis. You can display and manipulate within the Analysis editor, and subsequently incorporate, and subsequently incorporated into dashboards and agents.
- Select a subject area. If you purchased Oracle Business Intelligence Visual Analyzer, you can also select an external data source (uploaded Microsoft Excel file).
Specifying the Criteria for Analyses
This procedure is a step in the process for constructing an analysis.
See What Is the Process for Constructing Analyses?
You use the Criteria tab of the Analysis editor to specify the criteria for an analysis, including columns, filters, and selection steps. You also can specify:
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The default order in which columns are to be displayed in the analysis results
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The default sorting for the values that are displayed in views
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The default formatting (such as headings, number of decimal places, conditional formats, and system-wide defaults)
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Column formulas (such as adding a Rank or Percentile function)
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The aggregation rules for column totals
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The sort order for subject area folders and folder objects within the Subject Areas pane
You can now add views to the analysis.
Bear these guidelines in mind:
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In general, an analysis should contain at least one measure column. Measure columns are the key additive measurements of business performance, such as dollar sales per store, or the number of service requests opened and closed each day. An analysis without any measure columns generally is not meaningful, and can cause poor query performance or unexpected results. If you want to build an analysis without any measure columns, then first consult the administrator.
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Generally, you should avoid including both a hierarchical column and an attribute column that is also a lower level in the hierarchical column in the same analysis, unless you have selection steps that limit the data in the analysis to only the same level as the attribute column. If you do so, the data for all levels above the level that is the same as the attribute column will not be correct.
For example, suppose you have a hierarchical column named Merchandise Hierarchy that has these levels:
Total Product Brand LOB Product Type ProductAnd you have another attribute column named Product Name, which is the same level as the Product level. If you include both Merchandise Hierarchy and the Product Name attribute column in the same analysis, the data above the Product level will not be correct.
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If you add a column from the Subject Areas pane to the Selected Columns pane after displaying the analysis results, then the column is either included (that is, displayed in the view) or excluded from existing views, depending on the setting of the Display of Columns Added in Criteria tab option in the Data tab of the Analysis Properties dialog:
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The column is included in existing views as well as in any new views that you add, if the Display in existing and new views option is selected for the Display of Columns Added in the Criteria tab option.
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The column is excluded from existing views (that is, it is placed in the Excluded drop target of the Layout pane) but included in any new views that you add, if the Exclude from existing views, but display in new views option is selected for the Display of Columns Added in the Criteria tab option. See Understanding Drop Targets.
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Understanding Null Suppression
Content designers need to specify if null values will be presented.
By default, null measure values are suppressed for all analyses. As a content designer sometimes you may want to include null values in an analysis to contribute to the overall understanding of the data. You can set null values to display for the data at both analysis and view levels by selecting the Include Null Values option.
The following applies to an analysis or a view when the Include Null Values option is selected:
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Column members display.
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When drilling a column on the edge (one with null suppression turned off), null suppression is turned off for all columns on the edge.
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Member selection and conditions are applied to the edge and are filtered first.
Note:
Nulls might not display due to the applied condition. -
Calculated items, groups, totals, grand totals, showing data as a percentage, and server running aggregates, such as a running sum, are not affected by the Include Null Values setting.
Note:
Oracle BI Presentation Services sorts null values based on theNULL_VALUES_SORT_FIRST setting located in the NQSConfig.INI file.Note:
Null values will be suppressed in a table view if you have a criteria filter set on a measure.You cannot turn off null suppression for the following:
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Direct Database Requests. See Working with Direct Database Requests for additional information.
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Analyses that have been combined. See Combining Columns Using Set Operations for additional information.
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Hand-coded logical SQL analyses or any analysis that has been converted by using the Advanced Logical SQL settings. See Examining the Logical SQL Statements for Analyses for additional information.
To turn off null suppression at the analysis level:
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In the Criteria or Results tab, click the Analysis Properties button.
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Select the Include Null Values option to turn off null suppression for all views.
Note:
Null suppression can be overridden at the view level for funnels, gauges, graphs, tables, pivot tables, and trellises.
To turn off null suppression at the view level for funnels, gauges, graphs, tables, pivot tables, and trellises:
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Display the Results tab for the specific view. See Editing Views for additional information.
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Click the Properties button.
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Select the appropriate Include Null Values options for the view. For example, if you want to turn off null suppression for both rows and columns in a pivot table, select the Include rows with only Null values and Include columns with only Null values check boxes. This setting displays corresponding dimensions that have data, as well as null values.
Note:
If the view contains prompts or section edges, they also inherit the null suppression value from either the row or column edge.
Note:
Turning null suppression off may increase the volume of the data returned and impact performance. Contact your administrator for additional information.If results for analyses that include null values are not as you expect, ensure that data in your sources is consistent. See Ensuring That Data in Multiple Sources Is Consistent in Metadata Repository Builder's Guide for Oracle Business Intelligence Enterprise Edition for more information.
Example 1: Adding a Filter to a Row Edge on a Measure When Null Values Are Included
This example shows pivot tables with different row edge filtering options.
Suppose that you have a pivot table with Year on the row edge and Include Null Values selected in the Pivot Table Properties dialog (that is, nulls are not suppressed).
The illustration shows an example of T05 Per Name Year on the row edge with null values not suppressed.
The illustration shows an example of T05 Per Name Year on the row edge with null values not suppressed and a Criteria filter set to 2011 and 2012. When the Criteria filter is set for the attribute column, the filter is applied and the corresponding rows are removed from the analysis.
Example 2: Adding a Filter to a Row Edge on a Measure When Null Values Are Included
This example shows a pivot table with a filter applied to table data that includes nulls.
Suppose that you have the same pivot table with Year on the row edge and null values selected in the Pivot Table Properties dialog (the same criteria as indicated in Example 1: Adding a Filter to a Row Edge on a Measure When Null Values Are Included). This time however, you set the filter on the measure, 1 - Revenue, where the value is "greater than 23,000,000."
The illustration shows an example of the results. The Oracle BI Server returns all years (regardless of the measure filter) for unsuppressed edge queries. Because the measure value for T05 Per Name Year is equal to 23,000,000, the value for the row is filtered out, but the row still displays.
Example 3: Adding a "Not Null" Filter to a Row Edge on a Measure When Null Values Are Included
This example shows what is presented when a not null filter is applied to a pivot table with null values.
Suppose that you have the same pivot table with Year on the row edge and Include Null Values selected in the Pivot Table Properties dialog (the same criteria as indicated in Example 1: Adding a Filter to a Row Edge on a Measure When Null Values Are Included). This time however, you set the filter on the measure, 1 - Revenue, to not null.
The illustration shows an example of the results. The Oracle BI Server returns all values and honors the Include Null Values option for the member. Therefore, all rows with null values are displayed.
Editing the Formula for a Column
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 1 - Revenue column to show the revenue increased by 10%. The illustration shows an analysis in a pivot table that includes the 1 - Revenue column (which shows the revenue in the data source) and the 1 - Revenue Increased by 10% column, where the formula for the 1 - Revenue column was edited to calculate revenue increased by 10%.
Combining Columns Using Set Operations
You can display data in a different way by combining columns with 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 column for displaying the data in a different way.
Guidelines for Selecting Columns to Combine
You can combine columns if certain guidelines are met.
When selecting columns to combine, keep the following guidelines in mind:
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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.
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You can select columns from the same subject area or from a different subject area, but the columns must have some commonality.
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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.
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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
There are critical differences between Set operations and adding columns.
Combining columns using Set operations produces different results than adding columns from related subject areas:
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When you combine columns using Set operations, the analysis results show a single newly combined column governed by a Set operation. See Example: Combining Columns from One Subject Area.
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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.
Example: Combining Columns from One Subject Area
You can combine two columns from a single subject area to create comprehensive data sets.
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 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
You can combine columns from one or more subject areas.
Some steps include references to the example of creating the Offices & Departments column with the A-Sample Sales subject area.
Note:
Data formatting that has been saved as the system wide default for a column is not reflected in a combined column. If you want the data formatting in the combined column, then you must reapply it to the combined column.See the Data Format tab of the Column Properties dialog.Combining columns from multiple subject areas allows you to blend data so that it is more meaningful to users.
- Click the Results tab to view the columns in a table in the Compound Layout.
This figure shows how the combined Offices & Departments column is displayed in a table.
Viewing Metadata Information from the Subject Areas Pane
You can view metadata information for subject areas, folders, columns, and hierarchy levels.
Metadata information is helpful, for example, if you need a full description of one of these objects when creating an analysis.
Metadata information is contained in a metadata dictionary. A metadata dictionary is a static set of XML documents. Each XML document describes a metadata object, such as a column, including its properties and relationships with other metadata objects.
Saving Columns to the Catalog
You can save a column to the catalog for reuse in other analyses.
Saving a column is helpful for example, if you have edited the formula of a column or set column properties that you would like to save and reuse in other analyses.
When you save a column to the catalog:
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Oracle recommends that you save the column to the subject area folder. If a subject area folder does not exist in your /My Folders folder or within the /Shared Folders folder, then Oracle BI EE creates a subject area folder and the Save As dialog defaults a save path to /My Folders/Subject Area Contents/<subject area>. Saving the column to the subject area folders ensures that it is available when you build an analysis for the same subject area.
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Selections, filters, conditional actions links, and conditional formatting are not saved.
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References to the column are changed from the subject area to the catalog.
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You can reuse the saved column in another analysis by dragging and dropping it from the Catalog pane.
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You can edit the saved column from the Catalog pane, from the Catalog page, or within an analysis in which you reuse it.
Changes that you make from the Catalog pane and Catalog page apply to all versions of the saved column. Edits that you make within an analysis apply only to that analysis.
To save columns to the catalog, you must have been granted the Save Column privilege by the administrator.
To save a column to the catalog:
- In the Selected Columns pane, click the Options button beside the column that you want to save and select Save Column As. The Save As dialog is displayed.
- Complete the dialog to save the column to the catalog.
Changing the Sort Order of Subject Areas and Subject Area Folders
Users can alter the sort order of folders in the Subject Areas pane.
Subject area folders are displayed in the Subject Areas pane in the sort order specified in the Preferences tab of the My Account dialog by default. You can change this sort order for subject areas, subject area folders, and the columns within the subject area folders for all analyses (established in the Oracle BI metadata repository and initially set by the administrator) by modifying the Subject Area Sort Order option in the Preferences tab. See Changing the Sort Order of Subject Areas and Subject Area Folders.
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In the global header, click Signed In As username and select My Account.
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In the Preferences tab, click the Subject Area Sort Order list, and then make the appropriate selection. Your options follow:
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Sort A to Z (ascending)
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Sort Z to A (descending)
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Sort in Saved Order (The subject areas sort order specified in an analysis is saved as the default and subsequently used as the default sort order.)
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Default - Sort in Saved Order (The subject areas sort order is originally defined in the repository.)
Note:
The primary subject area specified when creating the analysis is always fixed at the top; that is, it will always display as the first subject area in the Subject Areas pane. For example, if you have three subject areas (A - Sample Sales, B - Sample Quotas, and C - Sample Headcount) participating in the analysis with C - Sample Headcount as the primary subject area, and the sort order is ascending, then the subject areas display as follows:-
C - Sample Headcount
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A - Sample Sales
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B - Sample Quotas
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Click OK to save your changes.
you can change the sort order for subject area folders and objects within each folder for the current analysis that you are creating or modifying.
- In the Subject Areas pane, add the appropriate subject areas.
- Click the Sort Subject Area button, and then select one of the following sort orders from the drop-down list
- Sort A to Z
- Sort Z to A
- Sort in Saved Order
- Complete your analysis.
Note:
Subject area sorts are not case-sensitive.Where Else is the Subject Areas Sort Order Reflected?
The Subject Areas sort order is shown in several parts of the interface.
In addition to seeing the subject areas sort order in the Subject Areas pane in the Criteria tab, you can also see the subject areas sort order that you configured in the Subject Areas pane reflected in the following places:
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Select the More columns option in the Filters pane to display the Select Column dialog. See Creating Column Filters.
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Select the More options in the Filters pane and select Add EVALUATE_PREDICATE function to display the New EVALUATE_PREDICATE Function dialog. See Working with the EVALUATE_PREDICATE Function.
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Select the More options button for a column in the Criteria tab, the Available pane displays in the Column Formula tab of the Edit Column Formula dialog. See Editing the Formula for a Column.
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Select the More options button for a column in the Criteria tab, the Column Formula tab of the Edit Column Formula dialog displays. Click the Filter button to display the Insert Filter dialog. See Editing the Formula for a Column.
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Select the Prompts tab of the Analysis editor, and then click the New button and select Column Prompt - More Columns to display the Select Column dialog. See Adding Prompts to Analyses.
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Click the Show/Hide Selection Steps Pane button to display the Selection Steps pane. Click Then New Step and select Apply a Condition. From the New Condition Step dialog, select a Condition Type to display the Measure drop-down list. See Creating Selection Steps.
You can also see the subject areas sort order in the subject areas pane within the Results tab. The Results tab reflects the subject areas sort order, but the values for columns are not sorted. The illustration shows an example of A - Sample Sales sorted in descending order. The values for D52 Country Name (for example, Australia, Brazil, China, and so on) are sorted in descending order.
Displaying the Results of Analyses
This procedure is a step in the process for constructing an analysis.
See What Is the Process for Constructing Analyses?
You use the Results tab of the Analysis editor to add different views of the results such as graphs, tickers, and pivot tables. 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:
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If the analysis contains only attribute columns and measure columns, then a table is displayed by default.
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If the analysis contains at least one hierarchical column, then a pivot table is displayed by default.
The administrator might have configured different views to display by default in the Results tab.
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. See Creating Additional Compound Layouts.
Creating 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.
Alerting Users to No Data in the Results of Analyses
You may want to change the default message for No Results to a user.
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.
- Edit the analysis to which you want to add a custom message.
- Click the Results tab of the Analysis editor.
- Click the Analysis Properties button.
- In the No Results Settings box of the Analysis Properties dialog, select Display Custom Message.
- In the Header field, enter the text of the header for the custom message.
- In the Message field, enter the explanatory text.
- Click OK.
Adding Prompts to Analyses
This procedure is a step in the process for constructing an analysis.
Use the Prompts tab of the Analysis editor to create prompts that allow users to select values to filter an analysis. Prompts allow users to select values that dynamically filter all views within the analysis. See What Is the Process for Constructing Analyses?
- Click the Prompts tab and add the prompt.
Examining the Logical SQL Statements for Analyses
This procedure is an advanced step in the process for constructing an analysis.
For information, see What Is the Process for Constructing Analyses?
You use the Advanced tab of the Analysis editor to examine the XML code and logical SQL statement that is generated for an analysis and optionally create an 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:
-
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 VARIABLEvar-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.
See Setting Administration Tool OptionsMetadata Repository Builder's Guide for Oracle Business Intelligence Enterprise Edition.
-
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 an 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 Oracle Fusion Middleware Logical SQL Reference Guide for Oracle Business Intelligence Enterprise Edition, 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.
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.Saving Analyses
This procedure is a step in the process for constructing an analysis.
See What Is the Process for Constructing Analyses?
You can save an analysis to a:
-
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.
-
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.
-
See Creating Agents.
- In the Analysis editor, click the Save Analysis 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 forecasted_sales.
- 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 forecasted_sales.mht.
- Click OK.
- Upload the .mht file to the Lotus Notes database.
- If you want to save the analysis to a Lotus Notes database using an agent:
- In the Save In box, select the folder in which to save the analysis.
- In the Name field, enter a name for the analysis, such as forecasted_sales.
- Click OK.
- Create the agent, scheduling it to be sent by email to a specific address on a Lotus Notes server.
Creating Agents from Analyses
You can create an agent directly from an analysis.
-
Creates a condition based on the analysis and adds the condition to the Condition tab of the Agent editor.
-
Adds the analysis as the content delivery on the Delivery Content tab of the Agent editor.
- Locate the analysis from which you want to create an agent using one of the following methods:
- Open the analysis, click the Results tab of the Analysis editor and then the Schedule button. The analysis is saved.
- Navigate to the analysis on the Catalog page, click the More list, and then click the Schedule option.
- Complete the following tabs of the Agent editor:
- General tab
- Schedule tab
- Condition tab
- Delivery Content tab
- Recipients tab
- Destinations tab
- Actions tab
- Save the agent.
Editing Analyses
You can edit any analyses in this manner unless you are in accessibility mode.
Note:
If you are using Oracle BI Enterprise Edition in accessibility mode, then you use the BI Composer wizard rather than the Analysis editor to edit analyses. See Using BI Composer to Work with Analyses and Accessibility Features.- In the global header, click Catalog to display the Catalog page.
- Navigate to the analysis to edit and click the Edit link.
- In the Analysis editor, make the desired changes to the analysis.
- Save the analysis.
About Embedding an Analysis in a Dashboard
Automatically show current analyses in dashboards by embedding.
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. See Adding Content to Dashboards.
Working with Direct Database Requests
Users with the appropriate privileges can create and issue a direct database request directly to a physical back-end database.
You can display and manipulate the results of the request within the Analysis editor, and subsequently you can incorporate the results into dashboards and agents. This section includes the following topics:
Setting Privileges for Direct Requests
Administrators can set access permissions for direct requests.
The following privilege settings in Oracle BI Presentation Services Administration control whether you can modify and execute direct requests:
-
Edit Direct Database Analysis
If this privilege is set for you, then you can create and edit direct database requests.
-
Execute Direct Database Analysis
If this permission is set for you, then you can issue direct requests and see the results.
See Managing Presentation Services Privileges in Security Guide for Oracle Business Intelligence Enterprise Edition.
Executing a Direct Database Request
You can query the database directly.
You can send a direct request to the database from Oracle BI EE.
Note:
Oracle BI Server security rules for data are bypassed and cannot be applied when direct database requests are issued from Oracle BI EE.
- In the global header, select New, then Analysis, then Create Direct Database Query.
- In the Criteria tab of the Analysis editor, specify the appropriate options.
- To issue the request and see the results, if any, click the Results tab.
Seeing the Results of Direct Database Requests
After you retrieve columns to create an analysis from a direct database request, you can work with that analysis similarly to how you work with other analyses.
The following list outlines differences with analyses whose columns originated from direct database requests:
-
The Subject Areas pane does not contain any columns, because you are not working with columns from a repository.
-
You cannot create groups or selection steps for this analysis.
-
You cannot specify conditional formatting for the columns.
-
The Sort Ascending (upward-facing triangle) and the Sort Descending (downward-facing triangle) that display when you hover the mouse over the area to the right of a column name in the header of a pivot table, table, or trellis view are not supported.
Using Variables
Variables help ensure that the same information and text is accurately presented in multiple places.
You can reference variables in several areas of Oracle BI Enterprise Edition, 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.
There are five types of variables that you can use:
-
Session
-
Repository
-
Presentation
-
Request
-
Global
What Are Session Variables?
A session variable is a variable that is initialized at login time for each user.
When a user begins a session, the Oracle BI Server creates a new instance of a session variable and initializes it.
There are as many instances of a session variable as there are active sessions on the Oracle BI Server. Each instance of a session variable could be initialized to a different value.
Session variables are populated by a query specified in an initialization block (INIT BLOCK). These session INIT BLOCKs are typically executed only once at login time. However, if the INIT BLOCK is marked as deferred, you can execute it at a later time when the relevant variables are referenced.
Session INIT BLOCKS are generally associated with a query statement, but can also be associated with an Lightweight Directory Access Protocol (LDAP) object. See Working with Initialization Blocks in Metadata Repository Builder's Guide for Oracle Business Intelligence Enterprise Edition.
There are two types of session variables:
-
System — A session variable that the Oracle BI Server and Oracle BI Presentation Services use for specific purposes.
System session variables have reserved names that cannot be used for other kinds of variables (such as static or dynamic repository variables and non-system session variables).
-
Non-system — A system variable that the administrator creates and names. For example, the administrator might create a SalesRegion non-system variable that initializes the name of a user's sales region.
The administrator creates non-system session variables using the Oracle BI Administration Tool.
Note:
Certain system session variables (such as, USERGUID or ROLES) cannot be overridden by request variables. Other system session variables, such as DATA_TZ and DATA_DISPLAY_TZ (Timezone), can be overridden if configured in the Oracle BI Administration Tool.See Working with Repository Variables in Metadata Repository Builder's Guide for Oracle Business Intelligence Enterprise Edition.
See About Session Variablesin Metadata Repository Builder's Guide for Oracle Business Intelligence Enterprise Edition.
What Are Repository Variables?
A repository variable is a variable that has a single value at any point in time.
There are two types of repository variables:
-
Static — Repository variables whose value persist and do not change until the administrator decides to change them.
-
Dynamic — Repository variables whose values are populated by a query specified in an INIT BLOCK. You can specify the interval for which the INIT BLOCK is refreshed. A dynamic INIT BLOCK automatically refreshes its variables' values at the specified interval.
Dynamic INIT BLOCKs can only be associated with a query (that is, by a SQL statement, an ADF query, or another supported data source). See Working with Initialization Blocks in Metadata Repository Builder's Guide for Oracle Business Intelligence Enterprise Edition.
The administrator creates repository variables using the Oracle BI Administration Tool.
See About Repository Variables in Metadata Repository Builder's Guide for Oracle Business Intelligence Enterprise Edition.
What Are Presentation Variables?
Presentation variables are created and used with dashboard prompts.
A presentation variable is a variable that you can create as part of the process of creating one of the following types of dashboard prompts:
-
Column prompt — A presentation variable that is 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.
-
Variable prompt — A presentation variable that is 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.
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 one or more values in the column or variable prompt, the value of the presentation variable is set to the value or values that the user selects.
What Are Request Variables?
Request variables temporarily override prompt database requests.
A request variable lets you override the value of a session variable but only for the duration of a database request initiated from a column prompt or a variable prompt.
Note:
Certain system session variables (such as, USERGUID or ROLES) cannot be overridden by request variables. Other system session variables, such as DATA_TZ and DATA_DISPLAY_TZ (Timezone), can be overridden if configured in the Oracle BI Administration Tool.See Working with Repository Variables in Metadata Repository Builder's Guide for Oracle Business Intelligence Enterprise Edition.
You can create a request variable as part of the process of creating one of the following types of dashboard prompts:
-
Column prompt — A request variable that is 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 request variable as part of a column prompt, in the New Prompt dialog (or Edit Prompt dialog), you have to select Request Variable in the Set a variable field and then enter the name of the session variable to override in the Variable Name field.
-
Variable prompt — A request variable that is 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 request variable as part of a variable prompt, in the New Prompt dialog (or Edit Prompt dialog), you have to select Request Variable in the Prompt for field and then enter a name of the session variable that you want to override in the Variable Name field.
The value of a request variable is populated by the column prompt 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 request variable is set to the value that the user selects. The value, however, is in effect only from the time the user presses the Go button for the prompt until the analysis results are returned to the dashboard.
Note:
Only string and numeric request variables support multiple values. All other data types only pass the first value.What Are Global Variables?
A global variable is a column created by combining a specific data type with a value.
The value is a string, number, date, time, expression, formula, and so on. You create a global value during the process of creating an analysis by using the Edit Column Formula dialog. The global variable is then saved in the catalog and made available to all other analyses within a specific tenant system.
You create a global variable as part of the process of creating an analysis.
Global variables are the following types:
-
Date
-
Date and Time
-
Number
-
Text
-
Time
The global variable is evaluated at the time the analysis is executed, and the value of the global variable is substituted appropriately. Only users with appropriate privileges can manage (add, edit, and delete) global variables.
Where Can I Reference Variables?
You can reference variables in many areas, but you can't reference all variables in each area.
You can reference variables in the following areas (but you can't reference all types of variables in each area):
-
Title views.
-
Narrative views.
-
Static text views.
-
Filters.
-
Column formulas.
-
Conditional formatting conditions.
-
Table and column headings in analyses.
-
Direct database requests.
-
Dashboard prompts and inline prompts.
-
Headers and footers for PDF output.
-
Link or image objects in a dashboard.
-
Text objects in a dashboard.
-
Graphs to specify conditional formatting of graph data.
-
Gauge thresholds.
-
Gauge limits.
-
Agents.
-
Actions to specify parameters.
-
Conditions to specify parameters.
-
Selection steps to override steps.
-
KPIs to define thresholds.
-
KPIs included in a KPI watchlist.
-
URL fields in dialogs.
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 table.
Note:
In the syntax, if the "at" sign (@) is not followed by a brace ({), then it is treated as an "at" sign.| Type of Variable | Syntax | Example |
|---|---|---|
|
Session |
@{biServer.variables['NQ_SESSION.variablename']}
where variablename is the name of the session variable, for example DISPLAYNAME. See About System Session Variables in Metadata Repository Builder's Guide for Oracle Business Intelligence Enterprise Edition. |
@{biServer.variables['NQ_SESSION.USER']}
|
|
Repository |
@{biServer.variables.variablename}
or
@{biServer.variables['variablename']}
where variablename is the name of the repository variable, for example, prime_begin. |
@{biServer.variables.prime_begin}
or
@{biServer.variables['prime_begin']}
|
|
Presentation or request |
@{variables.variablename}[format]{defaultvalue}
or
@{scope.variables['variablename']}
where:
Note: When using a dashboard prompt with a presentation variable that can have multiple values, the syntax differs depending on the column type. Multiple values are formatted into comma-separated values and therefore, any format clause is applied to each value before being joined by commas. |
@{variables.MyFavoriteRegion}{EASTERN REGION}
or
@{MyFavoriteRegion}
or
@{dashboard.variables['MyFavoriteRegion']}
or
(@{myNumVar}[#,##0]{1000})
or
(@{variables.MyOwnTimestamp}[YY-MM-DD hh:mm:ss]{)
or
(@{myTextVar}{A, B, C})
|
|
Global |
@{global.variables.variablename}
where variablename is the name of the global variable, for example, gv_region. When referencing a global variable, you must use the fully qualified name as indicated in the example. Note: The naming convention for global variables must conform to EMCA Scripting language specifications for JavaScript. The name must not exceed 200 characters, nor contain embedded spaces, reserved words, and special characters. If you are unfamiliar with JavaScripting language requirements, consult a third party reference. |
@{global.variables.gv_date_n_time}
|
You also can reference variables in expressions. The guidelines for referencing variables in expressions are described in the following table.
| Type of Variable | Guidelines | Example |
|---|---|---|
|
Session |
|
"Market"."Region"=VALUEOF(NQ_SESSION."SalesRegion") |
|
Repository |
|
CASE WHEN "Hour" >= VALUEOF("prime_begin") AND "Hour" < VALUEOF("prime_end") THEN 'Prime Time' WHEN ... ELSE...END
|
|
Presentation |
Note: If the @ sign is not followed by a {, then it is treated as an @ sign. When using a presentation variable that can have multiple values, the syntax differs depending on the column type. Use the following syntax in SQL for the specified column type in order to generate valid SQL statements:
|
"Market"."Region"=@{MyFavoriteRegion}{EASTERN REGION}
or
"Products"."P4 Brand"=(@{myTextVar}['@']{BizTech})
or
"Products"."PO Product Number"=(@{myNumVar}{1000})
or
"Sales Person"."E7 Hire Date"=(@{myDateTimeVar}{timestamp '2013-05-16 00:00:01'})
or
"Time"."Total Fiscal Time"=(@{myDateVar}{date '2013-05-16'})
or
"Time"."Time Right Now"=(@{myTimeVar}{time '00:00:01'})
For multiple values (in specified data types) when using SQL:
or
or
|
What Predefined Presentation Variables Are Available?
The application has a set of predefined presentation variables that you can use in various outputs.
This table 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}.)
Note:
For time zone variables, the time zone for a user must be set to a value other than Default in order for this variable to work. (Users set their preferred time zone in the Preferences tab of the My Account dialog.)| 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 |
|
session |
locale |
session.locale = en-us |
|
session |
language |
session.language = en |
|
session |
rtl
This indicates whether the language selection in the Login page is a right to left language. For example, if the language selection is Hebrew, then this variable returns true. |
session.rtl = false |
|
session |
timeZone |
session.timeZone = (GMT-06:00) Central America |
|
session |
timeZone.id
This returns a value that is not localized. |
session.timeZone.id = (GMT-06:00) Central America |
|
session |
timeZone.name
This returns a localized value. |
session.timeZone.name = (GMT-06:00) Central America |
|
session |
timeZone.value
This returns a localized value. |
session.timeZone.value = (GMT-06:00) Central America |
|
session |
loginTime |
session.loginTime = 6/29/2009 7:12:01 PM |
|
session |
logoutTime |
session.logoutTime = 6/29/2009 8:02:01 PM |
|
session |
lastAccessTime |
session.lastAccessTime = 6/29/2009 7:35:59 PM |
|
session |
currentUser |
session.currentUser = Administrator |
|
session |
currency.name |
session.currency.name = $ English - United States |
|
session |
currency.symbol |
session.currency.symbol = $ |
|
session |
currency.userPreference |
session.currency.userPreference = Global Currency 2 |
|
session |
accessibility
This indicates whether accessibility mode is enabled for the current user session. |
session.accessibility = true |
|
user |
id |
user.id = Administrator |
|
user |
displayName |
user.displayName = Administrator |
|
user |
homeDirectory |
user.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.currentPage |
dashboard.currentPage.name |
dashboard.currentPage.name = Sales page 1 |
|
dashboard.currentPage |
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.current Page |
dashboard.currentPage.currency.symbol |
dashboard.currentPage.currency. symbol = USD |
|
dashboard. current Page |
dashboard.currentPage.currency.userPreference |
dashboard.currentPage.currency.userPreference = 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
Referencing a variable in a title view ensures that the information is accurate.
Suppose that you have created a dashboard prompt called Region, which is based on the D50 Region column and which creates a presentation variable called MyFavoriteRegion as shown in the illustration.
Suppose also that you have created an analysis that shows Revenue by Region and Area. You have also added a filter on the D50 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, as shown in the illustration.
The illustration shows the results on a dashboard page when a user selects AMERICAS REGION in the Region prompt. Notice that the variable in the analysis title has been updated to the user's selection, AMERICAS REGION.
