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Attribute views are used to define joins between tables, and to select a subset (or all) of the table's columns and rows. The rows selected can also be restricted by filters. One application of attribute views is to join multiple tables together when using star schemas, to create a single dimension table. The resultant dimension attribute view can then be joined to a fact table via an analytic view to provide meaning to its data. In this use case, the attribute view adds more columns and also hierarchies as further analysis criteria to the analytic view. In the star schema of the analytic view, the attribute view is shown as a single dimension table (although it might join multiple tables), that can be joined to a fact table. For example, attribute views can be used to join employees to

organizational units which could then be joined to a sales transaction via an analytic view You have imported T009 and T009B tables for creating attribute view of type Time.

Use this procedure to create a view that is used to model descriptive attribute data (that does not contain measures) using attributes.

Tip: You need this view for creating a multidimensional view.

1. Set Parameter

1. In the Modeler perspective, expand the Content node of the required system.

2. Expand the package to which you want to save your information object.

3. In the context menu of Attribute Views node, choose New . 4. Enter a name and description for the view.

5. To create data foundation for the view, perform substeps of the required scenario given in the table below:

Scenario Substeps

Create a view with table attributes.

In the Sub Type dropdown list, choose Standard.

Scenario Substeps Create a view with time

characteristics. 1. In the Sub Type dropdown list, choose Time.

2. Select the required calendar type as follows:

a. If the calendar type is Fiscal, select a variant schema, and a fiscal variant.

b. If the calendar type is Gregorian, select the granularity for the data.

3. To use the system-generated time attribute view, select Auto Create.

Note:

The system creates a time attribute view based on the default time tables, and defines the appropriate columns/attributes based on the granularity. It also creates the required filters.

Note: The tables used for time attribute creation with calendar type Gregorian are, M_TIME_DIMENSION, M_TIME_DIMENSION_

YEAR, M_TIME_DIMENSION_ MONTH,

M_TIME_DIMENSION_WEEK and for calendar type Fiscal is M_FISCAL_CALENDAR. If you want to do a data preview for the created attribute view, you need to generate time data into the mentioned tables from the Quick Launch tab page.

Copy a view from an existing view – in this case, you can modify the copied view.

1. Choose Copy From.

2. Select the required attribute view.

Derive a view from an existing view – in this case, you cannot modify the derived view that acts as a reference to the base attribute view.

1. In the Sub Type dropdown, choose Derived.

2. Select the required attribute view.

6. Choose Finish.

The attribute view editor opens. The Scenario panel of the editor consist of two nodes - Data Foundation and Semantics. The Data Foundation node represents the tables used for defining the output structure of the view.

The Semantics node represents the output structure of the view, that is, the dimension. In the Details panel you define the relationship between data sources and output elements.

2. Define Output Structure

a) Add the tables that you want to use in any of the following ways:

○ Drag the required tables present in the Catalog to the Data Foundation node.

○ Select the Data Foundation node in the Scenario panel, and in the context menu of the Details panel, choose Add.

Note: You can choose to add the same table again in Data Foundation using table aliases in the editor. For example, in cases where you want to have different cardinalities from the same table.

Restriction: It is not allowed to add column views to the Data Foundation.

b) If you want to query data from more than one table, in the Details panel context menu, choose Create Join, and enter the required details.

Note: After creating the join, you can edit its properties like join type, cardinality, etc in the Properties view. You can choose to create Text Join between table fields in order to get language-specific data. For example, consider that you have a product table that contains product IDs and no description about products, and you have a text table for products that has a language-specific description for each product. You can create a text join between the two tables to get language-specific details. In a text join, the right table should be the text table and it is mandatory to specify the Language Column.

c) Add the table columns to the output structure that is, the Semantics node that you want to use to define attribute data. You can define the attribute data by doing one of the following:

○ Select the toggle button on the left of the table field.

○ Right-click the table field, and choose Add to Output.

d) If you want to specify a filter condition based on which system must display data for a table field in the output do the following:

1. Right-click the table field, and choose Apply Filter.

2. Select the required operator, and enter filter values.

All the table fields that you have added to the output are automatically mapped as attributes.

3. Define Key Attributes

a) Select the Semantics node.

b) In the Attributes tab page of the Column panel, select the required attribute and select the Type as Key Attribute.

Remember: If there is more than one key attribute, all key attributes of the attribute view must point to the same table in the data foundation. The central table of the attribute view is the one to which all the key attributes point.

Note: In case of auto-generated time attribute views, the attributes and key attributes are automatically assigned.

4. Optional Step: Create Calculated Columns

a) In the Output of Data Foundation panel, right-click Calculated Columns.

b) In the context menu, choose New.

c) Enter a name and description (label) for the calculated column.

d) Select a data type for the calculated column.

e) Enter length and scale for the calculated coulmn.

f) In the Expression Editor enter the expression. For example, you can write a formula such as,

if("PRODUCT" = 'ABC', "DISCOUNT" * 0.10, "DISCOUNT"). This means if attribute PRODUCT equals the string ‘ABC’ then DISCOUNT equals to DISCOUNT multiplied by 0.10 should be returned. Otherwise the original value of attribute DISCOUNT should be used.

Note: The expression can also be assembled by dragging and dropping the expression elements from the menus below the editor window.

g) Choose OK.

5. Optional Step: To filter and view the table data in the modeled view, which is relevant to a specific client as specified in the table fields, such as, MANDT or CLIENT, at runtime perform the following:

1. Select the Semantics node, in the Properties panel edit the Default Client property.

Note: The default value for the property is the one that is specified as a preference. At runtime, if the property is set to Dynamic then, the value set for the Session Client property is used to filter table data. The Session Client property is set while creating a user.

Note: You can choose to create hierarchies in order to define relationships between attributes.

Note:

You can choose to associate an attribute with another attribute, which describes it in detail. For example, when reporting via Label Mapping (also known as Description Mapping), you can associate Region_ID with Region_Text.

Before SP05, you could associate an attribute with another attribute in a model. In the runtime object an <attribute>.description column is generated and is shown during data preview. Now, from SP05 onwards the behavior is as follows:

○ For an attribute (CUSTOMER) you can now maintain label mapping by selecting another attribute (TEXT) from the same model as "Label Column" in the Semantics node. The result is

"TEXT" displaying as the label column in data preview. Note that the CUSTOMER.description column is not generated and is not shown in data preview anymore.

○ If you have created an object using the old editor (which supported the old style of description mapping) and try to open it using the new editor you will see a new column

CUSTOMER.description (as an attribute) which is hidden and disabled because this column cannot be used in other places such as parameter/variable, calculated column, restricted column and so on. You cannot maintain properties for this attribute for example, description.

CUSTOMER.description displays in the data preview as long as you do not change it in the editor. You can change its name. After changing the name you can maintain its properties and use it like other attributes.

6. Activate the view using one of the following options in the toolbar:

○ Save and Activate - to activate the current view and redeploy the affected objects if an active version of the affected object exists. Otherwise only current view gets activated.

○ Save and Activate All - to activate the current view along with the required and affected objects.

Note: You can also activate the current view by selecting the view in the Navigator panel and choosing Activate in the context menu.

You can find the activated model in the related package. If you want to modify this model, from the context menu, choose Open and make the necessary changes.

Restriction: The behavior of attribute views with the new editor is as follows:

● When an object (a table of an attribute view) is removed and added again in an attribute view in order to reflect the recently modified columns with its data type, it reflects the previous state of the columns alone. For more information, see SAP Note 1783668.

● When you open an attribute view and there is a missing column in the required object, an error is shown and the editor does not open. For information regarding the solution of this issue, see SAP Note 1788552.

Analytic views are used to model data that includes measures.

For example, an operational data mart representing sales order history would include measures for quantity, price, and so on.

The data foundation of an analytic view can contain multiple tables. However, measures that are selected for inclusion in an analytic view must originate from only one of these tables (for business requirements that include measure sourced from multiple source tables, see calculation view ).

Analytic views can be simply a combination of tables that contain both attribute data and measure data. For example, a report requiring the following:

<Customer_ID Order_Number Product_ID Quantity_Ordered Quantity_Shipped>

Optionally, attribute views can also be included in the analytic view definition. In this way, you can achieve

additional depth of attribute data. The analytic view inherits the definitions of any attribute views that are included in the definition. For example:

<Customer_ID/Customer_Name Order_Number Product_ID/Product_Name/Product_Hierarchy Quantity_Ordered Quantity_Shipped>

You can model the following elements within an analytic view:

● Columns

● Calculated Columns

● Restricted Columns

Remember: In the Semantics node, you can classify columns and calculated columns as type

attributes and measures. The attributes you define in an analytic view are Local to that view. However, attributes coming from attribute views in an analytic view are Shared attributes. For more information about the attributes and measures mentioned above, see section Attributes and Measures.

● Variables

● Input parameters

Note: For more information about the variables and input parameters mentioned above, see sections Assigning Variables and Creating Input Parameters.

You can choose to further fine-tune the behavior of the attributes and measures of an analytic view by setting the properties as follows:

● Filters to restrict values that are selected when using the analytic view.

● Attributes can be defined as Hidden so that they are able to be used in processes but are not viewable to end users.