6Adding Views for Display in Dashboards

Adding Views for Display in Dashboards

What are Views

Views use the presentation capabilities of Oracle BI Enterprise Edition to help you look at results of analyses in meaningful, intuitive ways. You can add a variety of views to the results, such as graphs and pivot tables that allow drilling down to more detailed information, explanatory text, a list of filters that were used to limit the results, and more.

When you display the results of a new analysis, the following views are displayed by default in the "Compound Layout" in the "Analysis editor: Results tab":

• A title view, which displays the name of the saved analysis.

• A table or pivot table view, which displays the results of the analysis, depending on the types of columns that the analysis contains:

  • If the analysis contains only attribute columns, only measure columns, or a combination of both, then a table is the default view.

  • If the analysis contains at least one hierarchical column, then a pivot table is the default view.

You can customize or delete the existing views for an analysis, add other views, and combine and position views anywhere in the pane.

Preparing multiple views of results can help you identify trends and relationships in data. If you are customizing results for display on a dashboard, then you can preview how the combination and position of views looks when viewed on a dashboard.

You can then save the analysis with the collection of views.

Related Topics

"What Types of Views Are Available?"

"Adding Views to the Results of Analyses"

"Editing Views"

"Saving a View"

"Removing a View"

View Types

Note: Your privileges control whether you can create all views or specific views only.

View Name	Description
Title	Displays a title, a subtitle, a logo, a link to a custom online help page, and timestamps to the results.
Table	Displays results in a visual representation of data organized by rows and columns. A table provides a summary view of data and enables users to see different views of data by dragging and dropping rows and columns.
Pivot Table	Displays results in a pivot table, which provides a summary view of data in cross-tab format and enables users to see different views of data by dragging and dropping rows and columns. Pivot tables structure data similarly to standard tables that contain column groups, but can display multiple levels of both row and column headings. Unlike regular tables, each data cell in a pivot table contains a unique value. By organizing data in this way, a pivot table is more efficient than a row-based table. Pivot tables are ideal for displaying a large quantity of data, for browsing data hierarchically, and for trend analysis.
Graph	Displays numeric information visually, which makes it easier to understand large quantities of data. Graphs often reveal patterns and trends that text-based displays cannot. However, when precise values are needed, graphs should be supplemented with other data displays, such as tables. A graph is displayed on a background, called the graph canvas. For the types and subtypes of graphs that are available, seeGraph Types .
Funnel	Displays results as a three-dimensional graph that represents target and actual values using volume, level, and color. Typically, funnel graphs are used to graphically represent data that changes over different periods or stages. For example, funnel graphs are often used to represent the volume of sales over a quarter.   Funnel graphs are well suited for showing actual compared to targets for data where the target is known to decrease (or increase) significantly per stage, such as a sales pipeline. In funnel graphs, the thresholds indicate a percentage of the target value, and colors provide visual information for each stage. You can click one of the colored areas to drill down to more detailed information. For the types of funnel graphs that are available, see Funnel Graph Types.
Gauge	Shows a single data value. Due to its compact size, a gauge is often more effective than a graph for displaying a single data value   Gauges identify problems in data. A gauge usually plots one data point with an indication of whether that point falls in an acceptable or unacceptable range. Thus, gauges are useful for showing performance against goals. Depending on the data in the analysis, a gauge view might consist of multiple gauges in a gauge set. For example, if you create a gauge view to show the sales data for the last twelve months, the gauge view consists of twelve gauges, one for each month. If you create one to show the total sales in the US, then the gauge view consists of one gauge. A gauge or gauge set is displayed on a background, called the gauge canvas. For the types of gauges that are available, see Gauge Types.
Trellis	Displays multidimensional data shown as a set of cells in a grid, where each cell represents a subset of data using a particular graph type. Data can be represented with graphs, microcharts, and numbers. The trellis view has two subtypes: Simple Trellis and Advanced Trellis. Simple trellis views are ideal for displaying multiple graphs that enable comparison of like to like. Advanced trellis views are ideal for displaying spark graphs that show a trend.   A simple trellis displays a single inner graph type, for example a grid of multiple Bar graphs. The inner graphs always use a common axis; that is to say, the graphs have a synchronized scale.   An advanced trellis displays a different inner graph type for each measure. For example, a mixture of Spark Line graphs and Spark Bar graphs, alongside numbers. In this example, the Spark Line graph might show Revenue over time, and the Spark Bar graph might show Units Sold. A measure column displaying numbers might be placed adjacent to the Spark Line graphs, showing the Revenue measure as a total value for a year. In an advanced trellis, each measure column operates independently for drilling, axis scaling, and so on. For definitions of microchart, and spark graph, see Trellis View Terms.
Filters	Displays the filters in effect for an analysis. Filters, like selection steps, allow you to constrain an analysis to obtain results that answer a particular question. Filters are applied before the query is aggregated.
Selection Steps	Displays the selection steps in effect for an analysis. Selection steps, like filters, allow you to obtain results that answer particular questions. Selection steps are applied after the query is aggregated.
Column Selector	Adds a column selector in the results. A column selector is a set of drop-down lists that contain pre-selected columns. Users can dynamically select columns and change the data that is displayed in the views of the analysis.
View Selector	Adds a view selector in the results. A view selector is a drop-down list from which users can select a specific view of the results from among the saved views.
Legend	Adds a legend to the results, which enables you to document the meaning of special formatting used in results, such as the meaning of custom colors applied to gauges.
Narrative	Displays the results as one or more paragraphs of text. You can type in a sentence with placeholders for each column in the results, and specify how rows should be separated.
Ticker	Displays the results as a ticker or marquee, similar in style to the stock tickers that run across many financial and news sites on the Internet. You can control what information is presented and how it scrolls across the page.
Static Text	A dds static text in the results. You can use HTML to add banners, tickers, ActiveX objects, Java applets, links, instructions, descriptions, graphics, and so on, in the results.
Logical SQL	Displays the SQL statement that is generated for an analysis. This view is useful for trainers and administrators, and is usually not included in results for typical users. You cannot modify this view, except to format its container or to delete it.

What Types of Views are Available

Graph Types

Note:

All graph types except for scatter, radar, and microchart can be 2-dimensional (2D) or 3-dimensional (3D). Not all types of graphs are appropriate for all types of data.

Graph Type	Description	Styles
Bar Subtypes available: Vertical Horizontal Stacked Vertical Stacked Horizontal	Shows quantities associated with categories. Bar graphs show quantities as bar lengths and categories as bars or groups of bars.   Bar graphs are useful for comparing differences among like items; for example, competing product sales, same product sales over different time periods, or same product sales over different markets. Can be used to compare measure columns by showing bars in a horizontal or vertical direction.	Rectangle Triangle Cylinder Diamond Gradient Pattern Fill
Line	Shows quantities over time or by category. Line graphs are useful for showing trends over time. Can be used to plot multiple measure columns.	Standard Line Stepped Line Curved Line
Area Subtypes available: Area 100% Stack	Shows the trend of the contribution of each value over time or by category.   It is a line graph for which the regions between lines are filled in. Regions stack, adding up to the total value for each time period or category.	Solid Fill Gradient Fill Pattern Fill
Pie	Shows data sets as percentages of a whole. Pie graphs are useful for comparing parts of a whole, such as sales by region or by district.	Solid Fill Gradient Fill Pattern Fill
Line-Bar Subtypes available: Standard Stacked	Plots two sets of data with different ranges, one set as bars, and one set as lines overlaid on the bars.   Line bar graphs are useful for showing trend relationships between data sets.	Rectangle Triangle Cylinder Diamond Gradient Pattern Fill
Time Series Line	Plots time series data. It scales the horizontal axis based on the time that has elapsed between data points.	Standard Line Stepped Line Curved Line
Pareto	Is a form of bar graph and line graph that displays criteria in descending order. In this graph type, the line shows a cumulative total of the percentages. Pareto graphs are useful for identifying significant elements, such as best and worst or most and least.	Rectangle Triangle Cylinder Diamond Gradient Pattern Fill
Scatter	Displays x-y values as discrete points, scattered within an x-y grid. It plots data points based on two independent variables. This enables you to plot large numbers of data points and observe the clustering of data points.   Scatter graphs are useful for observing relationships and trends in large data sets.	Standard Scatter Scatter-with-Lines
Bubble	Is a variation of a scatter graph that displays data elements as circles (bubbles). It shows three variables in two dimensions. One value is represented by the location of the circle on the horizontal axis. Another value is represented by the location of the circle on the vertical axis. The third value is represented by the radius of the circle. Bubble graphs are useful for plotting data with three variables, and for displaying financial data over a period of time.	None
Radar	Plots the same information as a bar graph, but instead displays data radiating from the center of the graph. Each data element has its own value axis. Radar graphs are useful for examining overlap and distribution.	None
Microchart Subtypes available: Spark Line Spark Bar Spark Area	A tiny graph (of similar size to a piece of nearby text) that displays only in the context of the trellis view and that is ideal for showing trend information.   A microchart graph type is useful within an advanced trellis, where data is displayed as a mixture of spark graphs and numbers. A microchart does not have axes or legends. Like larger graphs, a microchart's measure values are rendered as relatively sized bars (or lines, or area). Each measure name is displayed in its column header. Further details of the measure appear as tooltip text when you hover the mouse over a data cell.	None

Funnel Graph Types

Type	Description	Style
Standard	Uses a standard shape with equal stage widths.	Solid Fill Gradient Fill
Non-Standard	Uses a standard shape with unequal stage widths	Solid Fill Gradient Fill
Last-Stage Only	Uses a standard shape with equal stage widths. It is similar to standard funnel except that the target values of all the stages before the last is calculated based on the last stage target value and a constant called target factor.	Solid Fill Gradient Fill

What Types of Graphs are Available to Trellis Views

The list of graph types that are available for use in trellis views varies by trellis view subtype: Simple Trellis or Advanced Trellis.

The following types of graphs can be used in simple trellises:

• Bar (subtype Vertical)

• Bar (subtype Horizontal)

• Line

• Area

• Line-Bar

• Pie

• Scatter

• Bubble

The following visualization choices are available in advanced trellises:

• Numbers

• Microchart, of the following subtypes:

  • Spark Bar

  • Spark Line

  • Spark Area

For details on each graph type, see Graph Types.

Gauge Types

Note: All gauges are 2-dimensional (2-D) except for bulb gauges which can be either 2-dimensional or 3-dimensional (3-D). Their sizes can be small, medium, large, or a custom size.

Type	Description
Dial	Shows data using a dial arc with one or more indicators that point to to where the data falls within predefined limits.
Horizontal bar	Shows data using a horizontal bar that changes color to indicate whether the data is within predefined limits. The inner rectangle of the horizontal bar shows the current level of data against the ranges marked on an outer rectangle.
Vertical bar	Shows data using a vertical bar that changes color to indicate whether the data is within predefined limits. The inner rectangle of the vertical bar shows the current level of data against the ranges marked on an outer rectangle.
Bulb	Shows data using a circle that changes color to indicate whether the data is within predefined limits. Bulb gauges are useful when you must know what the status is, and not any indication of a specific value, or relative information about other threshold ranges.

Adding Views to the Results of Analyses

If you select a graph type or gauge type that is incompatible with the results, then no results are shown.

Do not use a pie graph to visualize data that includes negative values. Either filter the analysis to ensure that all values are greater than 0 or use a different graph type.

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

Note: The Author Limited does not have the permissions to create column selector, narrative, dashboard prompt, selection step, logical sql, view selector, or compound layout views.

Home > Oracle Business Intelligence > Catalog

1. In the "Analysis editor: Results tab" (or alternatively in the "Views pane"), click the New View toolbar button, then the type of view to create.

   The view is displayed in a container on the "Compound Layout". (Note that defaults are used to generate this view, such as the default view type, default formatting, and so on.)

2. To format the container for the view, click the Format Container toolbar button on the view container to display a formatting dialog.

   For information, see "Formatting Analyses, Views, and Dashboard Pages".

3. To edit the view, click the Edit View toolbar button on the view container.

   For more information, see "Editing Views".

4. Save the view. For information, see "Saving a View".

Editing Views

Note:

Each view type, except for a Logical SQL view, has its own editor in which you perform your edits. (You cannot edit a Logical SQL view. You can only format its container or delete it.)

Each view editor contains unique functionality for that view type but might also contain functionality that is the same across view types.

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

Note: When editing a view created by an Author Developer, an Author Limited cannot edit views he cannot create.

Home > Oracle Business Intelligence > Catalog

1. Open the analysis that contains the view to edit.

2. Click the "Analysis editor: Results tab".

3. Click the Edit View button for the view (or, alternatively, select the view in the "Views pane" and click the Edit View toolbar button).

   One of the following editors is displayed:

   • "Results tab: Column Selector editor"

   • "Results tab: Create Segment editor"

   • "Results tab: Create Target List editor"

   • "Results tab: Filters editor"

   • "Results tab: Funnel editor"

   • "Results tab: Gauge editor"

   • "Results tab: Graph editor"

   • "Results tab: Legend editor"

   • "Results tab: Narrative editor"

   • "Results tab: Pivot Table editor"

   • "Results tab: Selection Steps editor"

   • "Results tab: Static Text editor"

   • "Results tab: Table editor"

   • "Results tab: Ticker editor"

   • "Results tab: Title editor"

   • "Results tab: Trellis editor"

   • "Results tab: View Selector editor"

4. Using the editor for the view, make the appropriate edits.

   For additional guidance, see:

   • "Editing Table and Pivot Table Views"

   • "Editing Graph Views"

   • "Editing Trellis Views"

   • "Editing a Gauge View"

   • "Editing a Narrative View"

   • "Editing a Column Selector View"

   • "Editing a View Selector View"

   • "Editing a Legend View"

5. Click Done.

6. Save the view. For information, see "Saving a View".

Table and Pivot Table Views

Two common views for analyzing and displaying data are the table and pivot table.

These views share the same type of editor and much of the same functionality such as dragging and dropping, sorting, drilling, and conditional formatting. The following list provides some differences between the two types:

• Default View — When you create an analysis and display the results, the default view depends on the data in the analysis:

  • Table — If the analysis contains only attribute columns, only measure columns, or a combination of both, then a table is the default view.

  • Pivot Table — If the analysis contains at least one hierarchical column, then a pivot table is the default view.

• Drop Targets — Drop targets, which you use to modify column layout, differ slightly between the two views. Both views have the <view-type> Prompts, Sections, and Excluded drop targets. When multiple columns are placed in the <view-type> Prompts target, or page edge, each column displays its values in an individual drop-down list.

  • Table — Tables have columns and measures in the same drop target, and they do not have rows.

  • Pivot Table — Pivot tables can have rows, columns, and measures as separate drop targets.

• Drag and Drop Measure Columns — Measure columns behave slightly differently when you drag and drop them on tables and pivot tables.

  • Table — In tables, you can drag and drop measure columns in the middle of the table and they act as columns.

  • Pivot Table — In pivot tables, you can drag and drop measure columns in the middle and their labels can be in many locations.

• Column Names and Headings — You can turn off the display of the column headings in both tables and pivot tables. For column names, however, the following differences apply:

  • Table — In tables, you always see the names of columns.

  • Pivot Table — In pivot tables, you can turn off the display of the column names.

• Properties — The properties of the two views differ slightly, in areas such as paging controls and green bar formatting.

You can use the editors for the table and pivot table to customize the look and functionality of the view. The editors share much of the same functionality.

Editing Table and Pivot Table Views

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

Home > Oracle Business Intelligence > Catalog

1. Edit the analysis.

2. Display the "Results tab: Table editor" or the "Results tab: Pivot Table editor". For information, see "Editing Views".

3. To set the properties of the table or pivot table, click the Table View Properties or the Pivot Table View Properties button on the toolbar.

   The "Table Properties dialog" or the "Pivot Table Properties dialog" is displayed, where you can:

   • Specify formatting preferences for tables and pivot tables.

4. Use the buttons on the toolbar of the editor to perform functions such as the following:

   • To modify formatting, see "Adding Cosmetic Formatting to a Table or Pivot Table".

   • To add a group or calculated item to the view, see "Groups and Calculated Items".

   For more information on the buttons common to all views, see "Toolbar Buttons Common to All Views". For more information on specific data view buttons, see "Data View Editor Toolbar"

5. To affect the contents of the view, use the following panes:

   • The "Subject Areas pane"

   • The "Catalog pane"

   • The "Layout pane", as described in "Modifying the Layout of Data in Views"

   • The "Selection Steps pane"

6. To specify the sort order for a column in the view, see "Sorting Data in Views".

7. To drill in the data in the view, see "Drilling in Views".

8. Right-click to perform basic view interactions. For information, see "Right-Click Menu for Tables, Pivot Tables, and Trellises".

Adding Cosmetic Formatting to a Table or Pivot Table

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

Home > Oracle Business Intelligence > Catalog

1. Edit the analysis.

2. Edit the table or pivot table.

3. On the toolbar of the editor, click the View Properties button.

   The "Table Properties dialog" or "Pivot Table Properties dialog" is displayed.

4. To add green bar styling, click the green bar styling box.

   To change the way the styling is applied, select a style from the list.

5. To add cosmetic formatting, click the alternate formatting button.

   The "Edit Format dialog" is displayed.

6. To change the default green bar color, choose a new background color for the cell format. Make any other appropriate changes, and click OK.

7. In the Properties dialog, make any other appropriate changes, and click OK.

For more information about cosmetic formatting, see "Cosmetic Formatting"

The Visual Appearance of Graphs

You can format the visual appearance of graphs based on two settings:

• The position of the graph elements (such as lines or bars in a line-bar graph or slices in a pie graph).

  See "Graph Formatting Based on Position"

• Conditions applied to columns. See "Graph Formatting Based on Columns"

Graph Formatting Based on Position

Positional formatting enables you to customize the appearance of a graph based on the position of graph elements; that is, the numeric sequence in which graph elements (for example, bars) are displayed in a group. A group is determined by the attribute columns that are displayed in the Group By drop target area. (For information on drop target areas, see "Drop Targets".

For example, suppose that you have a bar graph whose criteria include the Region, Dollars, and Units columns. Dollars are displayed first, then Units in the Measures drop target area. Region is displayed in the Group By drop target.

You can format the visual appearance of a graph based on position in terms of its color, line width, and line symbols.

Graph Formatting Based on Columns

Conditional formatting enables you to customize the appearance of a graph based on conditions applied to columns. The formatting is applied to the column values that meet the condition.

You can specify a color in which to display graph data based upon a specific column value, or range of column values that meet the condition specified for the column.

For example:

• Conditionally changing the color of a graph based on specific column values.

  A user wants to create a bar graph to compare sales between two beverages, Lemonade and Cola. When creating a bar graph the user specifies two conditions, one where the bar representing Lemonade sales is yellow, and another where the bar representing Cola sales is blue.

• Conditionally changing the color of a graph based on a range of column values.

  A sales manager wants to create a bar graph to compare sales for all reps across two sales bands.When creating a bar graph the sales manager specifies two conditions, one where the bar is red for all sales reps with sales less than $250,000, and another where the bar is green for all sales reps with sales greater than $250,000.

The way that you specify conditional formatting for graphs is different from that used for other views. For information, see "Applying Conditional Formatting to Tables, Pivot Tables, and Trellises".

Formatting the Visual Appearance of Graphs

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

1. Edit the analysis.

2. In the results tab, click Edit View for the graph view.

3. Click Edit Graph Properties on the toolbar of the graph editor.

   The "Graph Properties dialog" is displayed.

4. Click the "Graph Properties dialog: Style tab".

5. Click the Style and Conditional Formatting button.

   The "Style and Conditional Formatting dialog" is displayed.

6. Click "Style and Conditional Formatting dialog: Style Formatting tab" to format the appearance of a graph based on position of the graph elements.

   To add a custom formatted position:

   1. Select the tab for the graph element (for example, bar) to which you want to add a custom formatted position.

   2. Click the Add new position button. A new position entry is displayed in the Custom Formatted Positions table.

   3. Specify the formatting. For example, to select the color to be applied to the position, click the down arrow next to the Color box to access the Color Selector dialog. (Note that the formatting options depend on the element.)

7. Click the "Style and Conditional Formatting dialog: Conditional Formatting tab" to format the appearance of a graph based on a condition that is applied to columns.

   To add a condition to a column:

   1. Click Add Condition Format and select the column to which you want to apply a condition.

      The "New Condition dialog" is displayed.

   2. Select the operator and enter a column value, or a range of column values for this condition.

   3. Click OK when you have finished.

      The new condition is displayed in the Conditional Formatting tab.

   4. To select the color to be applied to column values when the condition is met, click the down arrow next to the Color box to access the Color Selector dialog.

8. Click OK.

Rules for Applying Conditional Formats in Graphs

This is a short description.

The following rules apply for building and using conditions in graphs:

• Conditions can be created only from columns that are being used by the graph.

• When format conditions conflict with each other, conflicting conditions are prioritized in the following order:

  1. Conditional formatting on attributes

  2. Conditional formatting on measures

  3. Style formatting based on the positions of graph elements

• When a user drills on a graph that has conditional formatting applied, the following rules apply:

  • A conditional format based on measures is not carried to the next level. (It does not make sense to carry the conditional format to a different level; for example if, in a geographic hierarchy, from Region to City.

  • A conditional format based on attributes is carried to the next graph if it has not been drilled on.

    For example, if you had the conditional format "Lemonade=Blue" and only drill on years, then "Lemonade=Blue" stays in place.

Graph Exceptions for Conditional Formatting on Columns

Graph Type	Exception
Line Line-Bar Radar Time Series Line	Only symbol formatting is allowed for the line.
Pareto	Formatting is applied only to the bars, not to the Pareto line.

Editing Graph Views

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

Home > Oracle Business Intelligence > Catalog

1. Display the "Results tab: Graph editor". For information, see "Editing Views".

2. To set the properties of the graph, click the Edit Graph Properties button on the toolbar. The "Graph Properties dialog" is displayed, where you can set properties:

   • Related to the graph canvas, such as legend location

   • That control the appearance of the graph, such as the style

   • For axis limits and tick marks

   • That control the display of titles and labels for the graph

3. Use the buttons on the toolbar of the editor to perform functions such as printing a view, exporting a view, and so on.

   For more information on the buttons common to all views, see "Toolbar Buttons Common to All Views". For more information on specific data view buttons, see "Data View Editor Toolbar"

4. To affect the contents of the view, use the following panes:

   • "Subject Areas pane"

   • "Catalog pane"

   • "Layout pane", as described in "Modifying the Layout of Data in Views"

   • "Selection Steps pane"

   • (for funnel graphs and gauges only) "Settings pane" to set thresholds

5. To drill in the data in the view, see "Drilling in Views"

Trellis Views

A trellis view is a kind of graph view that displays a grid of multiple graphs, one in each data cell.

A trellis view falls into one of two subtypes:

• Simple Trellis. A simple trellis displays a core inner graph multiplied across row sets and column sets, rendering many small multiples that are ideal for comparing and contrasting.

• Advanced Trellis. An advanced trellis displays a grid of small spark graphs that are ideal for monitoring trends and spotting patterns in a data set.

Editing Trellis Views

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

Home > Oracle Business Intelligence > Catalog

1. Display the "Results tab: Trellis editor". For information, see "Editing Views".

2. To set the properties of the trellis, click the Trellis Properties button on the toolbar. The "Trellis Properties dialog" is displayed, where you can set properties:

   • Related to the grid canvas, such as legend location (simple trellis views only)

   • Related to graph size for the visualizations included in the trellis

   • That control whether the trellis listens to master-detail events

   • That control the appearance of the trellis's grid and its visualizations, such as various style choices and the way that legends are displayed

   • That control the type of scale and the appearance of scale markers for each of the trellis's visualizations (simple trellis views only)

   • That control the display of titles and labels (simple trellis views only)

3. Use the buttons on the toolbar of the editor to perform functions such as printing a view, exporting a view, and so on.

   For more information on the buttons common to all views, see "Toolbar Buttons Common to All Views". For more information on specific data view buttons, see "Data View Editor Toolbar"

4. To affect the contents of the view, use the following panes:

   • "Subject Areas pane"

   • "Catalog pane"

   • "Layout pane", as described in "Modifying the Layout of Data in Views"

   • "Selection Steps pane"

5. To specify the sort order for a column in the view, see "Sorting Data in Views".

6. Right-click to perform basic view interactions. For information, see "Right-Click Menu for Tables, Pivot Tables, and Trellises".

Trellis View Terms

Term	Definition
Visualization	In the context of Oracle BIEE, a visualization is the choice of graph that appears within a data cell in a trellis view. There are many visualizations from which to choose when creating a trellis view, including bar graphs, scatter graphs, and spark graphs.
Inner Graph	A nested graph, inside the grid of a trellis graph. Each inner graph has its own dimensionality as specified in the Visualization area of the Layout pane.
Outer Edge	The outer edges are the parts of a trellis view that border the inner graphs. These include the column and row headers, the section headers, and so on.
Simple Trellis	A trellis view that displays inner visualizations that are all the same type, such as all scatter graphs. The inner visualizations all use a common axis, also known as a synchronized scale.
Advanced Trellis	A trellis view that can display multiple visualization types within its grid, for example, Spark Line graphs, Spark Bar graphs, and numbers. Each visualization type displays a different measure.   You can think of an advanced trellis as a pivot table, except that for each measure you add to the pivot table, you can optionally associate a dimension and render that dimension as a spark graph visualization.
Synchronized Scale	( Applicable to Simple Trellis subtype only) A synchronized scale means that all the visualizations within the trellis are viewed on the same scale, that is, they share a common axis. Having a common axis makes all graph markers easy to compare across rows and columns.
Microchart	A tiny graph displayed in a grid along with other tiny graphs and numbers, comprising the data cell contents of an advanced trellis view. In Oracle BI EE, a microchart is always a spark graph.
Spark Graph	An embedded mini-graph that, in conjunction with other mini-graphs and numbers, illustrates a single trend. Spark graphs are also known as sparks.   Sparks do not include axes or labels; they get their context from the content that surrounds them. Each type of spark graph has only one measure, which is hidden; the scale is relative to itself only. A spark graph can be of the graph subtype Spark Line, Spark Bar, or Spark Area.

What Makes a Trellis View Unique

The trellis view, also referred to as a trellis graph, is the same as a pivot table—with one major exception: the data cells within the trellis contain graphs. Whereas a stand-alone graph type such as a single bar graph or a single scatter graph works on its own, the trellis graph works only by displaying a grid of nested graphs, known as inner graphs. So a bar-graph trellis view is actually comprised of multiple bar graphs.

In a general sense within the field of business analytics, a visualization is a visual representation of data, displayed in graphs, gauges, maps, and so on. In the trellis editor, within the Layout pane, there is a drop target called Visualizations; this is where you drag the columns for the inner graphs that will comprise the trellis you are building.

The visualizations that make up a simple trellis view are all graphs, and they can be many types of the existing stand-alone graphs described in "Graph Types". The visualizations that make up an advanced trellis are always microcharts, of the graph subtypes Spark Line, Spark Bar, or Spark Area. Ideally, the microcharts within an advanced trellis are placed alongside numbers (representing the same measure). For more information, see "What Are Microcharts".

What are the Functions of a Trellis View

This is a short description.

For the most part, a trellis view behaves like a pivot table, and the main difference between a trellis and a pivot table is the way the data cells appear.

In the row and column label cells of a trellis, you can:

• Right-click to do things like create groups and calculated items

• Right-click to hide or move measure labels

• Right-click to sort data

• Drag to reposition rows and columns

In the data cells of a trellis, you can hover the mouse pointer to show related contextual information.

Numeric data cells in a trellis behave the same as numeric data cells in a pivot table.

The ways in which the behavior of a trellis view differs from the behavior of a pivot table are the following:

• Graph data cells: There is no right-click functionality for the data cells in simple trellises, nor drilling in trellis graph data cells (left-click functionality).

• Microchart data cells: When you hover the cursor over the data cells in spark graphs, you are shown contextual information (such as first, last, minimum, and maximum values) that otherwise is not displayed as it would be in a pivot table view.

  Microcharts do not show axis labels, as regular charts do, so it is not immediately obvious what the inner graph dimensionality is. Use a microchart's tooltips to understand the data being represented inside the graph—sample individual values, as well as the overall dimensionality.

About Simple Trellis Versus Advanced Trellis

When you create a trellis view, the first thing you do is choose between two subtypes: Simple Trellis and Advanced Trellis. The Simple Trellis subtype displays a single type of inner visualization, for example, all bar graphs. The inner visualization always uses a common axis, so that all inner graphs are viewed on the same scale. (This concept of a common axis is also referred to as a synchronized scale.) Having a common axis makes all graph markers easy to compare across rows and columns.

The Advanced Trellis subtype allows for display of multiple visualization types within its grid. An advanced trellis illustrating sales trends might show a grid containing numbers in the cells of one column (revenue, for example), while another column alongside the numbers column displays Spark Line graphs in its cells, and those Spark Line graphs visualize the same measure as represented by the numbers (again revenue, but over a time period). Next to that column, a different microchart might be displayed, such as a column of Spark Bar graphs that visualize a different measure, such as unit totals.

Each measure that is visualized is assigned a different inner graph type. Each cell of the grid is scaled independently.

Think of an advanced trellis as a pivot table with spark graphs inside its data cells. But, for each measure you add, you can optionally associate a dimension and render it as a microchart visualization. This makes an advanced trellis very different from a simple trellis. In a simple trellis, all of the measures are rendered in the same visualization, along with additional dimensions.

What are Microcharts

A microchart is, as its name implies, a tiny chart. A microchart is displayed in trellis views of the Advanced Trellis subtype. A microchart is unique as a graph type in that it can never be an independent, stand-alone graph. It can be used only within the context of a trellis view, and its meaning comes only from the fact that it is one of many small multiples.

Spark Graphs in Oracle BI EE

A microchart can be one of several graph subtypes, including Spark Line, Spark Bar, and Spark Area.

Spark graphs, also called sparks, are unique from line graphs, bar graphs, and the other stand-alone graphs available in Oracle BI EE. Spark graphs are embedded mini-graphs that illustrate a single trend. Simple in their appearance, they do not include axes or labels and they get their context from the content that surrounds them. Each type of spark graph has only one measure, which is hidden; the scale is relative to itself only.

In Oracle BIEE, the visual appearance of sparks emphasizes trends, and within those trends, the highest and lowest values. Due to the condensed manner in which sparks display trends, it is possible for many trends to be compared (along with numeric values) on a single page.

While sparks are useful for certain types of analysis, such as high-level observation of trends and spotting of patterns, it is important to note that they do not illustrate the same specificity as their larger, fuller-featured counterparts.

Working with Spark Graphs

Sparks are unique mainly because they are small and because they can display a lot of information in a very constricted space. In Oracle BI EE, a spark graph is also different from a stand-alone graph in that, as a microchart, it can only be used within trellis views.

You can modify the size of your sparks in the same way that you do with full-featured graphs, in the Graph Properties dialog.

For more information about creating trellis views that include sparks, see "Design Considerations for Trellis Views and Microcharts".

Design Considerations for Trellis Views and Microcharts

The following are some ideas to consider when designing content displayed in trellis views:

• For comparisons, choose the Simple Trellis subtype.

• For trend analysis, choose the Advanced Trellis subtype.

• The inner graphs that make up a trellis should be readable and not too dense, so a trellis view is not especially useful for displaying multiple series or multiple groups. If you cannot easily target a data point with your mouse (to be shown a tooltip), then it is likely that the inner graph is too dense to be readable.

• When using the Simple Trellis subtype, note the following:

  • Designing a simple trellis is like designing a pivot table, except that the total number of cells that can be rendered is much less for a trellis.

  • The main difference between designing a simple trellis and designing a pivot table is that for a trellis, one or two of the dimensions can be associated with the visualization; so, that many less dimensions must be added to the outer edge.

  • It is best to design the trellis with a small number of outer-edge dimensions. The entire graph series should be visible at once (for easy comparison of like to like) with no need to scroll. If you must show additional dimensionality, consider adding the dimensions to the graph prompt.

  • When determining which data to show in column headers and which to show in row headers, the column headers should show one or two dimensions (each dimension with a small number of members). Most often, the dimension shown in column headers is time. Place the remaining dimensions in the row headers or in graph prompts.

• When using the Advanced Trellis subtype, note the following:

  • The key use case for an advanced trellis is to show trend graphs alongside numeric values, in a compressed form. So a typical advanced trellis contains a combination of spark graphs alongside number representations of the same measure.

  • Ideally, place no dimensions in the column headers, just place the measures here.

  • The dimensionality typically associated with a spark graph is time. As there are no visible labels in a spark graph, it is important that the data visualized is intrinsically ordered. For example, a spark graph visualizing regions would be meaningless, because the ordering of the regions (which would be the specific bars, in a Spark Bar graph) is unintuitive.

  • Just as when designing pivot tables, you generally display time on the horizontal axis, with the other dimensions displayed on the vertical axis. The eye then scans from left to right to see how the dimensionality changes over time.

When Might a Trellis Not Be the Best Visualization?

Hierarchical columns do not work well with the Simple Trellis subtype, because when a hierarchical column is displayed on the outer edge, parents and children (such as Year and Quarter) will by default be shown using a common axis scale. However, because Year and Quarter have different magnitudes, the markers in child graphs may be extremely small and hard to read against the parent scale. (Hierarchical columns do work well with the Advanced Trellis subtype, however, because each data cell is a different scale.)

Editing a Gauge View

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

Home > Oracle Business Intelligence > Catalog

1. Display the "Results tab: Gauge editor". For information, see "Editing Views".

2. To set the properties of the gauge view, click the Edit Gauge Properties on the toolbar. The "Gauge Properties dialog" is displayed, where you can set properties:

   • Related to the gauge canvas, such as, legend location.

   • That control the appearance of the gauge, such as the width and height of the gauge.

   • For gauge limits and tick marks.

   • That control the display of titles, footers, and labels for the gauge or gauges in a gauge set.

3. Use the buttons on the toolbar of the editor to perform functions such as printing a view, exporting a view, and so on.

   For more information on the buttons common to all views, see "Toolbar Buttons Common to All Views". For more information on specific data view buttons, see "Data View Editor Toolbar".

4. To affect the contents of the view, use the following panes:

   • "Subject Areas pane"

   • "Catalog pane"

   • "Layout pane", as described in "Modifying the Layout of Data in Views"

   • "Selection Steps pane"

   • "Settings pane" to set thresholds.

5. To drill in the data in the view, see "Drilling in Views".

Narrative Views

This is a short description.

You use a narrative view to provide information such as context, explanatory text, or extended descriptions along with column values.

In a narrative view, you can include values from attribute columns, hierarchical columns, and measure columns. For a hierarchical column, you can use selection steps to display hierarchy levels with the hierarchical column. For example, create a step to select members based on hierarchy and add members of the specified level. You cannot drill in narrative views.

Editing a Narrative View

You must have one of the following user type permissions:

• BI Author Developer

• BI Tenant Administrator

Home > Oracle Business Intelligence > Catalog

1. Display the "Results tab: Narrative editor". For information, see "Editing Views".

2. In the Narrative box, specify the columns to include in the view. To do so, use an at sign (@), optionally followed by a number. For example, include @2 to indicate the second column per the order of the column criteria.

3. Complete other fields as needed.

Column Selector Views

A column selector is a set of drop-down lists that contain pre-selected columns. Users can dynamically select columns and change the data that is displayed in the views of the analysis.

One drop-down list can be attached to each column in the analysis, and multiple columns can be attached to each drop-down list. You can create drop-down lists for attribute columns and measure columns. Updates that you make in the column selector view affect all the data views in the analysis.

You add columns to drop-down lists from the Subject Areas pane. When you add columns in this way, they are not added to the Criteria tab for the analysis. Instead, when you display the Criteria tab, you see that the column is now referred to as a "Column Group" with the default column for the list specified also. The default column is the one on which you created the drop-down list.

Editing a Column Selector View

You must have one of the following user type permissions:

• BI Author Developer

• BI Tenant Administrator

Home > Oracle Business Intelligence > Catalog

1. Display the "Results tab: Column Selector editor". For information, see "Editing Views".

2. Select Include Selector for each column for which you want to include a drop-down list of columns. Ensure that the column is highlighted in the editor.

3. To add a new column to a drop-down list, double-click the column to add in the "Subject Areas pane".

4. Complete the other fields as appropriate.

View Selector Views

This is a short description.

A view selector view enables users to select a specific view of the results from among the saved views for an analysis. When placed on a dashboard, the view selector is displayed as a list from which users can choose the view that they want to display below the selector.

Generally, you would include views in the view selector that are not being displayed in the Compound Layout view. For example, you might create a table, graph, gauge, and view selector view for an analysis, but include only the table and view selector view on the Compound Layout view. When the analysis is displayed on a dashboard page, users can select the graph or gauge view from the view selector view.

Editing View Selector Views

You must have one of the following user type permissions:

• BI Author Developer

• BI Tenant Administrator

Home > Oracle Business Intelligence > Catalog

1. Display the "Results tab: View Selector editor". For information, see "Editing Views".

2. In the Available Views list, select the views to be included in the view selector and move them into the Views Included list.

3. Complete other fields as needed.

Legend Views

You use a legend view to document the meaning of special formatting used in an analysis, such as the meaning of custom colors that are applied to gauges.

Editing a Legend View

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

Home > Oracle Business Intelligence > Catalog

1. Display the "Results tab: Legend editor". For information, see "Editing Views".

2. In the Captions box on the toolbar, select the position for the caption relative to the legend symbol.

3. In the Legend items per row box on the toolbar, select the number of legend items to display in a row.

4. For each legend item:

   1. In the Caption box, enter the meaning of the legend symbol.

   2. In the Sample Text box, enter the text that is to be displayed within the legend symbol.

   To add additional legend items, click Add Caption

5. Complete other fields as needed.

Modifying the Layout of Data in Views

Each editor for a data view contains the "Layout pane", except for the map editor. The Layout pane is displayed slightly differently for each view type, such as graphs and pivot tables. The Layout pane shows how the data in a view is laid out using drop targets. For more information on drop targets, see "Drop Targets".

You use the Layout pane to modify the way that data is arranged in the view. Specifically, you can:

• Modify the data in the view, as described in "Adding and Rearranging Columns in Views"

• Set properties, as described in "Setting Properties for View Bodies and Drop Targets"

• Add totals, as described in "Adding Totals to Tables and Pivot Tables"

• Display running sums and relative values, as described in "Displaying Running Sums and Relative Values for Measure Columns in Pivot Tables"

• Define section sliders in graphs and gauges, as described in "Defining a Section Slider in a Graph or Gauge"

• Add legends to graphs by selecting the Show In Legend box in the Layout pane

Drop Targets

In the Layout pane, the columns in a data view are displayed in drop targets. Drop targets indicate where a column can be inserted, moved to, or dropped. They represent a valid position for a column.

You use drop targets to modify the way data is arranged in a data view by dragging and dropping columns to different targets within the view.

Note: The Layout pane for trellis views is significantly different from the Layout panes that appear for other data views. For exceptions to the information provided here about drop targets, see "Drop Target Guidelines for Trellises".

Each data view contains the following drop targets:

• <view-type> Prompts — Provides an interactive result set that enables users to select the data that they want to view. The values from the columns that are displayed in this drop target are used as the initial criteria. In a view, these values are displayed in a drop-down list for selection, which is often referred to as the "page edge."

• Sections — Populates the areas that divide the view into sections. If you select the Display as Slider option in this drop target, then the values of the columns that are dropped in the Sections drop target are displayed as a section slider rather than as unique views.

• <view-type> area — Simulates the plot area or the body of the view itself and assists you in seeing what the view looks like.

  For tables, this area contains the Columns and Measures drop target, which contains all the columns in the view. In tables, all measures are treated as columns.

  For all other data views, this area contains the following drop targets:

  • Measures drop target — Populates the part of a view that contains summary data. Depending on the type of view, this area might include a single Measures drop target (for example, for pivot tables) or might contain subdrops targets (for example, the Bars (Y1-Axis) and the Lines (Y2-Axis) for line bar graphs). You drag and drop measure columns to these drop targets.

  • Other drop targets — Used to summarize the columns in the measure drop target or targets. You generally drag and drop attribute and hierarchical columns to these drop targets.

    The other drop targets that are displayed in a view depend on the type of view as described in Other Drop Targets in Data Views.

• Excluded — Excludes columns from the view results but leaves the columns as part of the analysis. See "Columns in the Excluded Drop Target" for more information.

In addition, each <view-type> area, except for the Table area and the Trellis area, contains the Measure Labels element. The Measure Labels element represents the labels for all the Measures columns in the drop targets in the Measures area. You can modify how measure labels are shown in a view by dragging it from one drop target and dropping it in another. For example, in a vertical bar graph, you can show each measure label in a different color by dragging and dropping the Measure Labels element to the Vary Color By drop target.

In pivot tables, you can also edit the format of measure labels or hide them.

Other Drop Targets in Data Views

Short sentence or paragraph to describe the configuration table.

View	Drop Target or Targets
Pivot table	Includes these drop targets: Columns — Shows a column in a column orientation. Pivot tables can contain multiple columns. Rows — Shows a column in a row orientation. Pivot tables can contain multiple rows.
Gauge	Rows — Shows the columns that are displayed in the gauges.
Bar, line, area, line-bar, time series line, pareto, scatter, or bubble graph	Includes these sub-drop targets within the main drop target (Bars, Line, Area, Bars & Lines, Line, Points or Bubbles). Target names differ depending on graph type. For example, Group By is Bubbles for bubble graphs and Points for scatter graphs. Group By — Clusters the axis labels into groups. For example in a vertical bar graph whose criteria includes the Region, District, and Dollars column, if this drop target contains the Regions column, then the data is grouped by region on the horizontal axis. Vary Color By — Shows each column value or column value combination in a different color.
Radar graph	Radar Sections — Shows column values as points on each line along a radius of the circle.
Pie graph	Includes these drop targets: Pies — Shows each column value or column value combination as a separate pie. Slices — Shows each column value or column value combination as a separate slice of the pie.
Funnel graph	Stage — Shows each column value as a stage in the funnel.
Trellis	Includes these drop targets: Columns — Shows a column in a column orientation. Trellises can contain multiple columns. Rows — Shows a column in a row orientation. Trellises can contain multiple rows. Includes these sub-drop targets within the main drop target Visualization for simple trellis views: Group By — Clusters the measures into groups Color By — Shows each measure in a different color, for example, in a vertical bar graph of a trellis, all the bars for Revenue are blue while the bars showing Billed Quantity are red. In a Scatter graph, use this drop target to vary the points by color. In a Bubble graph, use this drop target to vary the bubbles by color. Bar Axis — In a Line-Bar graph comparing revenue and billed quantity by region, shows one of two measures, such as Revenue. Line Axis — In a Line-Bar graph comparing revenue and billed quantity by region, shows one of two measures, such as Billed Quantity. Pies — In a Pie graph, shows measures as pies of varying colors. Slices — In a Pie graph, shows measures as slices of varying colors within pies. Points — In a Scatter graph, shows measures as points. Horizontal Axis — In a Scatter graph, shows one of two measures as a point. In a Bubble graph, shows one of three measures as a bubble. Vertical Axis — In a Scatter graph, shows one of two measures, such as a point. In a Bubble graph, shows one of three measures as a bubble. Bubbles — In a Bubbles graph, shows measures as bubbles of varying size. Size — In a Bubble graph, shows one of three measures as the size of a bubble. Includes these sub-drop targets within the main drop target Visualization for advanced trellis views: Areas — In a Spark Area microchart, shows columns displayed as areas. Bars — In a Spark Bar microchart, shows columns displayed as bars. Lines — In a Spark Line microchart, shows columns displayed as lines.

Columns in the Excluded Drop Target

A column in the Excluded drop target is not included in the view results but still remains as part of the analysis. A column can be placed in the Excluded drop target after views have been created for the analysis in various ways. A general rule is that a column is placed in the Excluded drop target for a view if it is not added explicitly to one or all views.

Some of the ways in which a column is placed in the Excluded drop target are as follows:

• You select the Exclude Column option from the right-click menu in a table or pivot table view.

• You select a column from the Subject Areas pane to add to views in an analysis as described in the following scenarios:

  • Criteria tab — If you add a column from the Subject Areas pane to the Selected Columns pane after displaying the analysis results and the Exclude from existing views, but display in new views option is selected for the Display of Columns Added in the Criteria tab property in the "Analysis Properties dialog: Data tab"), then the column is placed in the Excluded drop target of the existing views' Layout pane but included in any new views that you add.

  • Results tab — The behavior might differ depending on whether you add columns to a view editor or to the Compound Layout

    • View Editor — If you add a column from the Subject Areas pane to a view, then you place the column in that view. The column is placed in the Excluded drop target for all other views in the analysis.

    • Compound Layout — If you double-click a column in the Subject Areas pane, then you place the column in a default drop target of the Layout pane for all existing views in the current Compound Layout. The column is placed in the Excluded drop target for all other views in the analysis.

      If you drag and drop a column from the Subject Areas pane to a tabular view, then you place the column in the drop target for that view. The column is placed in the Excluded drop target for all other views in the analysis.

If you want a column that is in the Excluded drop target to be displayed in a view, then you can easily move it. Simply display the Layout pane for the view, and drag and drop the column from the Excluded drop target to the desired one.

Excluding columns differs from removing columns. You can use the Remove Column option from the More Options button in the Layout pane for a view to remove a column entirely from the analysis.

Aggregation and the Excluded Drop Target

In a pivot table or graph that includes columns in the Excluded drop target, an aggregation rule is applied to aggregate measures in the data body of the view to a single value. For example, suppose that you have the following columns in the Selected Columns pane:

Region	City	Dollars
East	NY	1000
East	Boston	500

If the City column is placed in the Excluded drop target, then the view typically displays the following:

East 1500

The aggregation rule is applied to aggregate 1,000 and 500 into 1,500. In a pivot table or graph, the aggregation rule that is specified in the "Edit Column Formula dialog" applies. For a pivot table, you can select a specific aggregation rule using the More Options menu in the Layout pane.

Suppose that you wanted the table, pivot table, or graph to show the following values:

East 1000

East 500

To achieve this aggregation, include both Region and City columns in the view layout, but hide the City column using its "Column Properties dialog: Column Format tab"

Drop Target Guidelines for Graphs and Funnel Graphs

The following restrictions and guidelines apply to dragging columns from one drop target and dropping them in another in graphs and funnel graphs:

• A bubble graph requires at least three measures. Plot one measure on the horizontal axis, another measure on the vertical axis, and a third measure on the bubble size axis.

• In a bubble, line-bar, scatter, or funnel graph, you cannot drag and drop measure labels.

• A pareto graph can have only one measure.

  If you drop another measure on the Measures drop target, then the measures are swapped; that is, the existing measure is replaced by the newly dropped measure and is moved automatically to the Excluded drop target.

• A time series line graph requires a single date or date-time data column to be selected on the horizontal axis. It has a single vertical axis, but supports multiple data series.

• A scatter graph requires at least two measures. For example, you can plot one measure column on the horizontal axis and another measure column on the vertical axis. These measures are plotted for values on the Group By axis.

• A funnel graph uses two measures but only one is required. If you do not select a second measure, then the first measure is used for the second measure. If you have selected two measures and then select a new measure, then the new measure replaces the measure currently in the Actual Measures drop target.

Drop Target Guidelines for Trellises

The following guidelines apply to working with drop targets in trellises:

• Expanding Drop Targets in Trellises — The Layout Pane of the trellis editor is notably different in that it is vertical rather than horizontal. When drop targets contain more data than can be shown within this more narrow Layout Pane, the drop target containers expand on hover. That is, when you hover the mouse over an overfilled drop target, you see the complete selection of measures and columns, and you can move and reorder even the measures and columns that were not visible before the hover. The chosen item appears with a slight transparency as you drag it.

• Placement of Measures — The following are considerations to keep in mind as you work with measure columns in trellis views:

  • Measures can be reordered within the measures drop targets by dragging left or right.

  • In simple trellis views, measure columns can be placed only on the measure edge of Color By or Group By.

  • In some simple trellis visualizations (scatter graphs, bubble graphs, and line-bar graphs), you can swap measures. If you drag an existing measure from one axis and drop it in the target for another axis, the two measures' positions in the view are swapped.

  • Scatter graphs, bubble graphs, and line-bar graphs allow a single measure per axis, as follows:

    • Scatter graph drop targets: Horizontal Axis, Vertical Axis

    • Bubble graph drop targets: Horizontal Axis, Vertical Axis, Size

    • Line-bar graph drop targets: Bar Axis, Line Axis

  • In advanced trellis views, measures comprise the innermost column headers of the trellis.

  • When moving measures from the Color By drop target to or from the Group By drop target:

    • Dragging a single measure moves all the measures along with it. (This is known as sticky behavior.)

      Exception: Upgraded analyses automatically select the Treat as an Attribute Column box for measures (in the "Edit Column Formula dialog: Column Formula tab"), so this sticky measure behavior only happens if you deselect the Treat as an Attribute Column box for existing measures.

    • Dragging a new measure into the view moves all existing measures to wherever you place the new measure.

  • To place a measure on the non-measure edge of a visualization, or in the Rows target or Columns target, you must first convert the measure to an attribute column. You do this in the "Edit Column Formula dialog: Column Formula tab".

  • Attribute columns can be dragged out of the Measures drop target without causing the drop target or the measures inside it to move with the attributes.

Adding and Rearranging Columns in Views

Using the view editor and the Layout pane, you can easily modify and change the order of columns in the following ways:

• Drag and drop columns in tables and pivot tables to the desired positions in the editor using the handles and drop targets. For example, if you have two columns in the Rows section of a pivot table, reverse the order of the columns by dragging and dropping the first column after the second one. In a table, you can drag and drop columns, but you cannot stack columns, as you can in a pivot table.

  You can also drag and drop columns in this way in the Compound Layout.

• Drag and drop columns in the Layout pane. A target is active and ready for the "drop" when it appears highlighted. When you hover the mouse pointer over a column in the Layout pane, the cursor state changes from the pointer to a move cursor when you can "grab" the column and move it over a drop target. For example, you can easily move a column in a pivot table from the Rows drop target to the Sections target to create a unique pivot table for each value in the column.

  For details of dragging and dropping columns in the Layout pane of the trellis editor, see "Drop Target Guidelines for Trellises".

  For more information on drop targets, see the "Layout pane".

• Add a column to a graph or gauge. To do so, drag the column from the Subject Areas tab to the appropriate location in the view editor or to a drop target in the Layout pane.

• Remove columns in the Layout pane. For example, you can easily remove a column in a view by selecting Remove Column from the More Options button. Removing columns differs from excluding columns, which is described in "Drop Targets".

This list provides only a partial summary of where dragging and dropping can occur. You can drag and drop columns and catalog objects whenever you see the "Subject Areas pane" and "Catalog pane". You can also drag and drop items in views on dashboards. See "Columns in the Excluded Drop Target" for more information.

Setting Properties for View Bodies and Drop Targets

In the Layout pane, you can click the Properties button to display a dialog in which you specify properties for the view body (such as Pivot Table) or for a drop target (such as Sections). For example, you can specify whether to display headings, set background color, and insert page breaks.

Modifying the View Body or Drop Target Properties

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

Home > Oracle Business Intelligence > Catalog

1. Edit the analysis.

2. In the results tab, click Edit View for the view.

3. In the view editor, display the Layout pane and click the Properties button beside the view body (for tables and pivot tables) or drop target name.

   The Properties dialog for the view body or drop target is displayed.

4. Complete the appropriate fields and click OK.

Adding Totals to Tables and Pivot Tables

In the Layout pane, you can add totals for columns in tables and pivot tables. You can position the totals at various locations in the view. You can add totals for columns that are displayed on the various edges. For each measure, the total uses the aggregation rule for that measure. The default aggregation rule for a measure column is specified in the Oracle BI repository, or by the original author of the analysis.

If you specify a total in the Rows or Columns drop target of a pivot table, then the totals that are displayed are the result of the columns that are specified in the Measures drop target. Total values are not displayed on the Columns or Rows edges of the pivot table but rather in the data in the center of the pivot table.

Adding Totals to a View

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

Home > Oracle Business Intelligence > Catalog

1. Edit the analysis.

2. In the results tab, click Edit View for the view.

3. Display the "Layout pane" for the view.

4. To add grand totals to the entire view:

   • For a table, in the Columns and Measures drop target, click the Totals button, then click the location such as Before.

   • For a pivot table, in the Rows or the Columns drop target, click the Totals button, then click the location such as After.

5. To turn on and off the totals that apply to all the values in the drop target, click the Totals button beside the drop target name, such as Sections. Then select the location for the total, such as Before the data items. A totals area is added to the view.

Displaying Running Sums and Relative Values for Measure Columns in Pivot Tables

You can use the Layout pane to display running sums or the relative value of measure columns in pivot tables, as described in the following sections.

Running Sums for Measure Columns in Pivot Tables

In a pivot table, you can display numeric measures as running sums, where each consecutive cell for the measure displays the total of all previous cells for that measure. This option is a display feature only that has no effect on actual pivot table results.

Typically, running sums are displayed for duplicated attribute columns or for measure columns for which the option to show data as a percentage of the column has been selected, with the last value being 100 percent. Running sums apply to all totals. The running sum for each level of detail is computed separately.

Column headings are not affected when the running sum option is selected. You can format the column heading if you want it to indicate that the running sum option is in effect.

The following usage rules are in effect for running sums:

• A running sum is incompatible with the SQL RSUM function (the effect would be a running sum of the running sum).

• All running sums are reset with each new section. A running sum does not reset at a break within a section or continued across sections.

• If a measure does not display in a single column or in a single row, then the measure is summed left to right and then top to bottom. (The lower right cell contains the grand total.) A running sum does not reset with each row or column.

• You cannot specify rolling minimums, maximums, and averages in the Layout pane. You can include these if administrators create formulas for them in the metadata repository.

Displaying a Measure as a Running Sum

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

Home > Oracle Business Intelligence > Catalog

1. Edit the analysis.

2. In the results tab, click Edit View for the view.

3. In the Layout pane for the pivot table, in the Measures area, click the More Options button for the row or column to be summed and select Display as Running Sum.

Relative Values for Measure Columns in Pivot Tables

In a pivot table, you can dynamically convert a stored or calculated measure into a percent or an index. This shows the relative value of the item, compared to the total, without the need to explicitly create a calculated item for it. You can view the measure as a percentage between 0.00 and 100.00, or as an index between 0 and 1.

For example, if you are using a pivot table to examine sales by product, then you can duplicate the sales measure and view it as a percentage of the total. This enables you to see the actual sales, and the percentage of sales, that each product accounts for.

Showing an Item as a Relative Value

The analysis you are creating or editing must include a heirarchical column.

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

Home > Oracle Business Intelligence

1. Edit or create an analysis that contains a heirarchical column.

2. In the results tab, edit a pivot table.

3. Note: The following step is optional. When you duplicate the measure column in the pivot table, you can see both the total for the measure and its relative value. This eliminates the need to add the column twice on the Criteria tab to see the total and its relative value in the pivot table.

   In the Layout pane for the pivot table, click the More Options button for the item you want to show as a relative value.

4. To duplicate the measure column, select Duplicate Layer.

   The measure is displayed a second time in the pivot table, with the same name.
5. Note: The Show Data As option is available only for items that are stored or calculated measures.

   Click the More Options button, select Show Data As, select Percent of or Index of, then select the appropriate option.

6. To rename the new column, click the More Options button, select Format Headings and in the Edit Format dialog, enter a value in the Caption field.

Selection Sliders in Graphs and Gauges

A section slider displays members of one or more attribute or hierarchical columns as values on a rectangular bar and provides mechanisms to select a value. You use a section slider to limit the data that is shown in a graph or gauge.

A section slider consists of the following components:

• Slider bar — Displays the members of one or more attribute or hierarchical columns as values along a rectangular bar.

  Note: The administrator configures the maximum number of values that can be displayed on the slider bar. However, you can set a maximum lower than the system maximum by setting the Maximum number of section slider values field in the "Section Properties dialog".

• Slider thumb — Indicates the current value of the section slider. You can drag the thumb to the desired value.

• Decrease button — Moves the slider thumb to the value to the left of the current value.

• Increase button — Moves the slider thumb to the right of the current value.

• Play button — Sequentially moves the thumb through the slider values. After being clicked, the play button changes to a pause button to allow you to stop on a particular value.

Defining a Section Slider in a Graph or Gauge

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

Home > Oracle Business Intelligence > Catalog

1. Open the analysis that contains the graph or gauge in which you want to define a section slider.

2. Click the "Analysis editor: Results tab".

3. Click the Edit View toolbar button in the graph, funnel, or gauge view.

   The "Results tab: Graph editor", "Results tab: Funnel editor", or "Results tab: Gauge editor" is displayed.

4. For each column to be displayed on the section slider, in the "Layout pane" of the Graph or Gauge editor, drag and drop the column to the Sections drop target.

5. Select Display as Slider.

6. Click Done to close the editor and return to the Results tab.

7. Click the Save Analysis toolbar button to save your changes.

Interactions in Views

You can specify the types of interactions that are to occur when users:

• Click in a data view. See "Click Interactions in Views".

• Right-click in a table, pivot table view, or trellis at runtime. See "Right-Click Interactions in Views"

Click Interactions in Views

You can specify the types of interactions that are to occur when users click (that is, left-click) in a data view. The following types of interactions are available:

• None — Specifies that nothing happens when users click the column heading or a value. This option turns off drilling for attribute columns; it does not turn off drilling for hierarchical columns.

• Drill — Enables users to drill down to more detailed information. See "Drilling in Views".

• Action Links — Enables users to click a hot spot in a data view and then select an action link to execute an action, such as navigating to a saved analysis. See , "Actions"."

• Send Master-Detail Events — Sends master -detail events in a master-detail relationship. See "Master-Detail Linking of Views" and "Linking Views in Master-Detail Relationships".

You create interactions at the criteria level using the "Column Properties dialog: Interaction tab". Interactions that you create take effect in all data views (that is, tables, pivot tables, graphs, funnel graphs, trellises, gauges, and maps).

Right Click Interactions in Views

You can specify the types of interactions that are to occur for an analysis when users right-click in a table, pivot table view, or trellis at runtime. The following right-click interactions are available:

• Drill (when not a primary interaction)

• Move Columns

• Sort Columns

• Add/Remove Values

• Create/Edit/Delete Groups

• Create/Edit/Delete Calculated Items

• Display/Hide Sub-totals

• Display/Hide Running Sum

• Include/Exclude Columns

You enable right-click interactions for an analysis using the "Analysis Properties dialog: Interactions tab". Right-click interactions that you enable take effect in all data views except graphs, gauges and maps.

Note: Right-click interactions are not available for graphs in trellis views (Simple Trellis and Advanced Trellis), but you can right-click on text in the trellis view in the same manner as in a pivot table. This includes data cells in an advanced trellis that have Visualization set to Text.

Drilling in Views

If the administrator has configured columns for drilling in the subject area, then you can allow users to drill in data in tables, pivot tables, graphs, trellises, gauges, and maps. Drilling is a way to navigate through data in views quickly and easily.

In Which Columns Can I Drill

You can drill in attribute columns and hierarchical columns. For information on drilling in columns, see "Drilling in Results".

How Do I Allow Drilling in Columns

As the content designer, you specify whether users can drill in particular columns in views on dashboards. You control whether drilling is allowed in particular columns by specifying options in the "Column Properties dialog: Interaction tab".

If drilling is not a primary interaction of a particular column (as set in the "Column Properties dialog: Interaction tab"), then you can allowing drilling as a right-click interaction in table and pivot table views at runtime. To do so, you select the Drill (when not a primary interaction) option in the "Analysis Properties dialog: Interactions tab".

Effects of Drilling on Filters and Selection Steps

This is a short description.

Drilling in columns affects their filters and selection steps as described in the following list. For more information, see "Filtering and Selecting Data for Analyses."

• Hierarchical columns: No steps are added to the selection when you expand or collapse members in a hierarchical column. That is, the expanding and collapsing does not change the selection of data for the column.

  For example, suppose that you create a pivot table in which you select 2008 as the only member in the Time dimension, and you arrange the data so that this one Time member is the column header in the pivot table. You can expand to show quarters in 2008 and then the months in the last quarter. At this point the pivot table has child members for 2008, Q1 2008, Q2 2008, Q3 2008, Q4 2008, October 2008, November 2008, and December 2008. If you display the Selection Steps pane, however, you see that the selection for the Time dimension still contains only the 2008 member.

  Expanding and collapsing in a hierarchical column affects only that particular view. No other views are affected.

• Attribute columns: You can drill down from the row heading or column heading or from a member in an attribute column. Drilling on a heading adds the lower level to the view. Drilling on a member adds the lower level and affects both filters and selection steps:

  • Drilling on a member adds a filter for the current member, thereby limiting the results. For example, if you drill on the Game Station member in a table that includes the P1 Product column, you add the E1 Sales Rep Name column, which adds a filter that specifies that P1 Product equals Game Station.

  • Drilling on a member adds the lower-level column to the analysis and updates the column in the Selection Steps pane without providing a step update during design.

Sorting Data in Views

When you create an analysis, you can specify sorting for the results that are displayed in graphs, pivot tables, tables, and trellises.

Common Sort Functionality

Some sort functionality is common to graphs, pivot tables, tables, and trellises, although you cannot sort values interactively on a graph.

Graphs, pivot tables, tables, and trellises reflect the sorting that you specify in the Selected Columns pane of the Criteria tab for attribute columns and hierarchical columns. Use the Sorting menu options that are available from the Options button for a column. These sorting options provide the ability to sort by a single column or by multiple columns, each in either ascending or descending order. By creating sorts for multiple columns, you can specify multiple level sorts such as second-level and third-level sorts of the data. These sort levels are indicated with numbers and a sort icon for the column in the Criteria tab.

The options that you specify on the Criteria tab serve as the initial sorting state of the analysis. You can modify the sort later when the analysis is used in a table, pivot table, or trellis.

When you sort items in a hierarchical column, you always sort within the parent. That is, children are never sorted outside of their parent. The children are displayed below the parent in the proper sort order; the parent is not sorted within its children.

If you include totals in a view, then those values are not sorted. They remain in the location in which you placed them, regardless of sorting.

If you sort an attribute column or hierarchical column that includes a group, then that group is always displayed at the bottom of the list. If there are multiple groups, then the groups are listed in the order in which their corresponding steps are specified in the Selection Steps pane.

Sorting Data

Sorting allows you to quickly sort rows and columns of a view, either from lowest to highest data values, or from highest to lowest. You can also return the order to the order in the data source by clearing all sorts. You can specify alphanumeric sorts on the row and column edges of pivot table views, table views, and trellis views.

You make sorting specifications in various ways, including those in the following list:

• the column and select the appropriate sort option from the menu. For more information on the sorting options menu, see "Sorting Options Menu".

• Right-click in a pivot table, table, or trellis, select Sort or Sort Column (whichever is available), and then select the appropriate sorting option from the menu. For more information on the sorting options menu, see "Sorting Options Menu".

• Hover the mouse pointer over the area to the right of the column name in the header of a pivot table, table, or trellis and click either the upward-facing triangle (Sort Ascending) or the downward-facing triangle (Sort Descending) for that column. These sort specifications override those that you make with the right-click menu.

  If you see a shaded-in sort button in the column header or the row header, then you know that the column contains a primary sort. You can add a second-level or third-level sort by hovering over another innermost column header or row header and clicking the appropriate sort button or clicking the right-mouse button.

Any sorting options that you specify in a view override those that were made in the Selected Columns pane.

Clearing Sorts

You can use various methods to clear sorts:

• For sorts that have been applied in the Selected Columns pane, click Clear All Sorts in All Columns. The sort specifications that you made in the Selected Columns pane are removed. Sorts that you specified in a view remain.

• For sorts that have been applied directly in a pivot table, table, or trellis:

  • Click a sort button in an unsorted column to remove the primary sort from the column to which it now applies and apply it to the column whose button you just clicked.

  • Select the Clear All Sorts in View option from the right-mouse menu.

Saving a View

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

Note: You can save a view that you are working with at any time. To save a view, you must save the analysis.

If you want to save a view:

• In a new analysis or in an existing analysis, click the Save Analysis toolbar button in the "Analysis editor"

• In an existing analysis that you must save as another name, click the Save As toolbar button in the "Analysis editor"

If this is the first time that you are saving the analysis, or you are saving an existing analysis as another name, then the "Save As dialog" is displayed where you specify the information for saving the analysis.

Renaming a View or Compound Layout

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

1. If you want to rename:

   • A view, on the toolbar of the view's editor, click the Rename View button.

   • A compound layout, in the "Analysis editor: Results tab" toolbar, click the Rename Compound Layout button.

   The "Rename View dialog" is displayed.

2. Enter the new name for the view or compound layout.

3. Click OK.

Rearranging a View

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

Home > Oracle Business Intelligence > Catalog

1. Edit the analysis.

2. In the results tab, place the cursor just inside the top edge of the view to be rearranged.

3. Click and hold the left mouse button (if configured) on the view.

   The view is displayed as a transparent movable object.

4. Use the mouse to drag the view to the required position.

   A view can be rearranged as follows:

   • At the boundary of a view.

     Each view has four boundaries (upper, lower, left or right), and can be arranged alongside the boundaries of other views in an analysis. You can drag and drop a view to display it next to the boundary of another view.

   • At the outer boundary of the compound layout.

     Each compound layout contains one or more views arranged in either a square or a rectangle. The compound layout has four boundaries (upper, lower, left and right). You can drag and drop a view to the outer boundary of an compound layout. This action displays the view across the entire length or breadth of the compound layout.

5. Release the mouse button.

   The view is rearranged in the compound layout in the position that is indicated by the blue bar.

6. Repeat the previous steps to rearrange further views.

Printing Views

This is a short description.

You can print views using HTML or Adobe PDF (Portable Document Format). Adobe Reader 6.0 or greater is required to print using Adobe PDF.

You can also specify PDF and print options, including adding headers and footers. For more information on printing, see "Changing Print Options for a View"

Note: The HTML method of printing relies on the print handling capabilities of your browser. If you do not get the results you want, then choose PDF to open and then print the analysis.

You can print:

• A single view from the view's editor

• An assemblage of views that are displayed in the "Compound Layout"

Printing a Single View or an Assemblage of Views

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

Home > Oracle Business Intelligence > Catalog

1. Create or edit an analysis.

2. If you want to print:

   • A single view, on the toolbar of the view's editor, click the Print this analysis button.

   • The assemblage of views displayed in the Compound Layout, in the "Analysis editor: Results tab" toolbar, click the Print this analysis button.

3. Select Printable HTML or Printable PDF.

   • For HTML, a new window shows the view or views to print.

     From the File menu, select Print.

   • For PDF, an Adobe PDF window shows the view or views to print.

     Use the options that are available in the Adobe PDF window to save or print the file.

Changing Print Options for a View

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

Home > Oracle Business Intelligence > Catalog

1. Create or edit an analysis.

2. In the "Analysis editor: Results tab" toolbar, click the Print Options button.

   The "Print Options dialog" is displayed.

3. Specify the appropriate options in the dialog, and click OK.

Previewing How a View is Displayed on a Dashboard

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

If you want to preview:

• A single view, on the toolbar of the view's editor, click the Show how results will look on a Dashboard button.

• The assemblage of views that are displayed in the Compound Layout, in the "Analysis editor: Results tab" toolbar, click the Show how results will look on a Dashboard button.

The dashboard preview is displayed in a new window. Any prompts are displayed and applied in the preview.

Removing a View

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

If you want to remove a view from:

• A compound layout, in the view in the "Compound Layout", click the Remove View from Compound Layout button.

  Removing a view from a compound layout removes it only from the compound layout, not from the analysis.

• An analysis, in the "Views pane", select the view and then click the Remove View from Analysis toolbar button.

  Removing a view from an analysis removes the view from the analysis and from any compound layout to which it had been added.

Refreshing the Results in a View

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

In the "Analysis editor: Results tab" toolbar, click the Refresh the results of the current analysis button.

Customizing Views for Delivery to Agent Users

Delivery content is automatically formatted based on the destinations that you specify in the "Agent editor: Destinations tab". You can manually control which view or views are sent to a particular destination by including them in a compound layout.

Controlling which Compound Layout is Sent to a Destination

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

1. For the appropriate analysis, create and save the view to use for delivered content.

2. On the toolbar of the "Analysis editor: Results tab", click the Create Compound Layout button.

3. Add the view that you created to the newly created compound layout.

4. On the toolbar of the "Analysis editor: Results tab", click the Edit Analysis Properties button.

5. In the View for Text Delivery box, select the compound layout to which you added the view.

6. Click OK.

Master-Detail Linking of Views

This is a short description.

Master-detail linking of views enables you to establish a relationship between two or more views such that one view, called the master view, drives data changes in one or more other views, called detail views.

Suppose you have the following two views:

• A table that shows Number of Applicants by State

• A graph that shows Number of Applicants by Source with State on a section slider

Using the master-detail linking functionality, you can link the two views so that when you click a particular State in the table, the State on the section slider of the graph as well as the data in the graph changes to reflect the State that was clicked on the table.

Note: Master-detail linking is not supported when you click the plot area of a graph.

Master Views

This is a short description.

A master view drives data changes in one or more detail views. A view becomes a master when you set up the interaction of a column in the view to send master-detail events on designated channels. This column is known as the master column.

The master column is the column whose values when clicked send a master-detail event, which passes information to update the data in a detail view.

The following types of views can be master views:

• Graph

• Funnel graph

• Gauge

• Map

• Pivot table

• Table

• Trellis (only the outer edges, not the inner visualizations)

A master view can be in the same analysis as the detail view or in a different analysis. A master view can update data in one or more detail views.

What Types of Columns Can Be Master Columns

Any type of column — hierarchical, attribute, or measure — can be a master column. However, the master column cannot be displayed on the page edge or the section slider in the master view. It must be displayed in the body of the view.

Note: While hierarchical columns are supported as master columns, if a user clicks a hierarchy level, then a change takes effect in the detail view only if the level is expanded there as well.

What Information Do Master-Detail Events Pass

When a master-detail event is raised, it passes the definition of the cell (or item) in the master view that was clicked. The specific information it passes depends on the type of column:

• For an attribute column, it passes the clicked value and the contextual information to the left and above the axis member.

  For example, in Master-Detail Event Information for an Attribute Columnif the attribute column District is the master column, and you click CHICAGO DISTRICT (Bold), then the master-detail event information passed is Region=CENTRAL REGION and District=CHICAGO DISTRICT.

  Master-Detail Event Information for an Attribute Column
  Region	District	Year	Hires
  Central Region	Chicago District	1998	30
  		1999	45
  		2000	53
  	Cincinati District	1998	7
  		1999	20
  		2000	13
  	Kansas City District	1998	51
  		1999	43
  		2000	46
  Eastern Region	Boston District	1998	17
  		1999	37
  		2000	25
  	New York District	1998	50
  		1999	52

• For a measure column, it passes all the contextual information for the data value.

  For example, in Master-Detail Event Information for a Measure Column, if the measure column Hires is the master column, and you click 53 (Bold), then the master-detail event information passed is Region=CENTRAL REGION, District=CHICAGO DISTRICT, and Year=2000.

  Master-Detail Event Information for a Measure Column
  Region	District	Year	Hires
  Central Region	Chicago District	1998	30
  		1999	45
  		2000	53
  	Cincinati District	1998	7
  		1999	20
  		2000	13
  	Kansas City District	1998	51
  		1999	43
  		2000	46
  Eastern Region	Boston District	1998	17
  		1999	37
  		2000	25
  	New York District	1998	50
  		1999	52

The master-detail event information defines the position of a page edge or a section slider in a detail view.

What Are Channels

A channel links a master view to a detail view. It is the vehicle that carries master-detail events from the master view to the detail view. The same channel must be used for both the master view and the detail view in a master-detail relationship, for example, Channel1.

Detail Views

A detail view is a view that listens for and responds to master-detail events sent by a master view on a specified channel. A view becomes a detail view, when you set up the view to listen to master-detail events.

A detail view includes one or more columns whose values are changed directly by the information passed by a master-detail event. These columns are known as detail columns. A detail column must:

• Be on the page edge or the section slider of the detail view. Whether a detail column is displayed on the page edge or a section slider, depends on the type of view:

  • For pivot tables and tables, it must be on the page edge

  • For graphs and gauges, it can be on the page edge, or on the section slider

    Note: If a detail column is on a section slider and a page edge is present, the detail column is not updated by the information in the master-detail event. For example, if the information in the event is Region=CENTRAL and Year=2009, and the page edge of the detail view has Region and the section slider has Years, then only the Region is updated.

  • Match a column in the master view

When a detail view contains multiple detail columns, it is listening for and responding to master-detail events on multiple channels.

The following types of views can be detail views:

• Graph

• Funnel graph

• Gauge

• Table

• Pivot table

• Trellis (only the outer edges, not the inner visualizations)

A detail view:

• Can listen for master-detail events from multiple master views

• Can be in the same analysis as the master view or in a different analysis

• Cannot act as a master to another view

How Does Master-Detail Linking of Views Work

When a master view and a detail view are linked and a user clicks a cell (or item) in the master column, a master-detail event is generated on the designated channel and the master-detail event information is passed to the detail view.

The detail view (which is listening on the designated channel for master-detail events) receives the event and the master-detail event information. It reviews the master-detail event information to determine which column in this information matches the detail column. When it finds a match, it takes the column values in the master-detail event information, updates the detail column, and then refreshes the entire detail view.

Note: If a master column is on a section slider, then any detail view that is not visible initially in the browser is not updated and refreshed until the view becomes visible when you scroll down. For more information on section sliders, see "Defining a Section Slider in a Graph or Gauge".

Linking Views in the Master-Detail Relationships

To link views in master-detail relationships, you must define the master view and the detail view.

For more information on master-detail linking, see "Master-Detail Linking of Views"

Defining the Master View

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

1. Create or edit the analysis whose view (or views) you want to define as the master view.

2. For the column that is to be the master column, in the "Analysis editor: Criteria tab", click the Options button and then select Column Properties.

   The "Column Properties dialog" is displayed.

3. Click the "Column Properties dialog: Interaction tab".

4. In the Primary Interaction box in the Value area, select Send Master-Detail Events.

   The Specify channel field is displayed.

5. In the Specify channel field, enter a name for the channel on which the master view sends master-detail events. You can use any appropriate name, for example, Sales Analysis Channel, Channel 1, or channel 1.

6. Add the view to use as a master view. Note that any view that you add that includes the master column can be used as a master view.

7. Click OK.

Defining the Detail View

You must have one of the following user type permissions:

• BI Author Limited

• BI Author Developer

• BI Tenant Administrator

1. Create or edit the analysis whose view you want to define as the detail view.

2. Create the view that is to be the detail view, for example, a graph.

3. Edit the view.

4. Click the view's Property button on the toolbar of the view's editor. For example for a graph, click the Graph Properties toolbar button in the Graph Editor.

   The view's property dialog is displayed, for example, for a graph, the "Graph Properties dialog".

5. In the "Graph Properties dialog: General tab" (for graph or funnel graph), the "Gauge Properties dialog: General tab" (for a gauge), the "Table Properties dialog: Style tab" (for a table), or the "Pivot Table Properties dialog" (for a pivot table, select the Listen to Master-Detail Events box.

6. In the Event Channels field, enter the name of the channel on which the detail view is to listen for master-detail events. The name must match (including case) the name of the channel on which the master view sends master-detail events. (For more information on the channel in the master view, see "Defining the Master View".

7. Click OK.

8. Note: You must drag and drop all detail columns to the same drop target. You cannot have some on the Prompts drop target and some on the Section drop target.

   In the "Layout pane", drag the column (or columns) to be the detail column (or columns) to either the Prompts drop target or the Section drop target as follows:

   • If the detail view is a pivot table or table, then drag the column or columns to the Prompts drop target.

   • If the detail view is a graph or a gauge, then drag the column or columns to the Prompts drop target or to the Sections drop target, whichever you prefer, unless the graph or gauge has a column on the Prompts drop target. Then, you must drag the column or columns to the Prompts drop target.

   For more information on drop targets, see "Drop Targets"

9. If you dragged and dropped one or more detail columns to the Section drop target, then select the Display as Slider box.