Working with Logical Dimensions - 11g Release 1 ()
logical relationships between hierarchy levels according to the procedures required for 2 The Spatial MultiDimER model. Key attribute. Other attributes. Level a) b) A dimension includes either a level, or one or more hierarchies. Levels are the one that does not have a parent level is called root and represents the most. When I move the cursor over it I get the message, "Attribute relationships do not exist between one or more levels in this hierarchy message.". Why do I need to create attribute relationships in my SQL Server Analysis If that level is not available and if the attribute relationship is setup properly For our date example, I actually create a new data dimension, Date TEST, SSAS warns us within a hierarchy if no relationship exists, but once created.
These databases are available for download, free, on CodePlex at: For our date example, I actually create a new data dimension, Date TEST, based on the existing date view in the data source view. As shown below, year, quarter, month, week, and date were added as attributes to the dimension.
As noted in the second screen print, SSAS displays a caution triangle and displays a warning that says that no attribute relationships exists in the hierarchy and as such performance may be impacted. Thus, we now know we should create an attribute relationship, but it is not required.
To create the relationship we need to click on the Attribute Relationship tab. As shown in the following illustration, we currently do have attribute relationships setup; however all of them directly link each attribute back to the Date Key, the lowest level. No intermediate relationships exist to efficiently rollup the hierarchy levels.
You will also notice that the attribute relationship tab contains two views. The graphical view is on the top of the below screen print while the detail list view is shown at the bottom. In order to create a relationship, we can either use the drag and drop method or manual add method via the tool bar button. As shown below, to create our relationship, we actually start at the lowest level and then drag our attribute to the next higher level value.
In our example, the Date key is already related to each of the four attributes in the hierarchy. As such, our first level of the relationship, which is Date Key to Week, has already been established. As you can see in the below illustration, we now have established the Date Key to Calendar Week to Calendar Month relationships. Figure Sample Dimensional Cube Dimensions do not have to be defined, but spending time creating them can yield significant benefits because they help query rewrite perform more complex types of rewrite.
They are mandatory if you use the Summary Advisor to recommend which materialized views to create, drop, or retain. Dimension values are usually organized into hierarchies.
Going up a level in the hierarchy is called rolling up the data and going down a level in the hierarchy is called drilling down the data. In the video chain example: Within the time dimension, months roll up to quarters, quarters roll up to years, and years roll up to all years. Within the product dimension, products roll up to categories, categories roll up to departments, and departments roll up to all departments.
SQL Server Analysis Services Attribute Relationships
Figure Geography Dimension Data analysis typically starts at higher levels in the dimensional hierarchy and gradually drills down if the situation warrants such analysis. You can visualize the dimensions of a business process as an n-dimensional data cube. In the video chain example, the business dimensions product, location, and time can be represented along the three axes of the cube. Each unit along the product axis represents a different product, each unit along the location axis represents a store, and each unit along the time axis represents a month.
At the intersection of these values is a cell that contains factual information, such as units sold and profits made. Higher-level analysis consists of selecting and aggregating the factual information within a subcube, such as rentals of comedy videos in California stores during the second quarter of For example, city is a child of state because you can aggregate city-level data up to stateand state. Using this approach, you should find it easier to translate this into an actual dimension.
In the case of normalized or partially normalized dimensions a dimension that is stored in more than one tableidentify how these tables are joined.
Note whether the joins between the dimension tables can guarantee that each child-side row joins with one and only one parent-side row. In the case of denormalized dimensions, determine whether the child-side columns uniquely determine the parent-side or attribute columns. You must not create dimensions in any schema that does not satisfy these relationships, incorrect results can be returned from queries otherwise.
Drilling Across Drilling across is when you change what you are viewing to another hierarchy at the same level. The caveat with drilling across is that you will not necessarily enter another hierarchy at the same level, which means the totals may be different. In the Chronological Key dialog, select a chronological key column, click Up or Down to reorder the column, and then click OK. You should use this only to specify a drill path that is outside the normal drill path defined by the dimensional level hierarchy.
It is most commonly used to drill from one dimension to another. You can delete a logical level from a drill path or reorder a logical level in the drill path. To add a dimension level to the preferred drill path: Click Add to open the Browse dialog, then select the logical levels to include in the drill path.
You can select logical levels from the current dimension, or from other dimensions. Click OK to return to the Level dialog. The names of the levels are added to the Names pane. Automatically Creating Dimensions with Level-Based Hierarchies You can set up a dimension automatically from a logical dimension table if a dimension for that table does not exist.
To create a dimension automatically, the Administration Tool examines the logical table sources and the column mappings in those sources and uses the joins between physical tables in the logical table sources to determine logical levels and level keys. Therefore, it is best to create a dimension in this way after all the logical table sources have been defined for a dimension table.
The following rules apply: Create Dimensions is only available if the selected logical table is a dimension table defined by 1: N logical joins and no dimension has been associated with this table. An automatically created dimension uses the same name as the logical table, adding Dim as a suffix. For example, if a table is named Periods, the dimension is named Periods Dim.
When there are multiple tables in a source, the join relationships between tables in the source determine the physical table containing the lowest-level attributes. For example, the lowest level of the periods table is Periods Detail. The logical key of the dimension table is mapped to the lowest level of the hierarchy and specified as the level key. This logical column should map to the key column of the lowest level table in the dimension source. If there are two or more physical tables in a source, the columns that map to the keys of those tables become additional logical levels.
These additional level names use the logical column names of these key columns. The order of joins determines the hierarchical arrangement of the logical levels. The level keys of these new logical levels are set to the logical columns that map to the keys of the tables in the source.
If there are multiple logical table sources, the tool uses attribute mappings and physical joins to determine the hierarchical order of the tables in the physical sources.
For example, you might have three sources A, B, C each containing a single physical table and attribute mappings for 10, 15, and 3 attributes, respectively not counting columns that are constructed from other logical columns. The following is a list of the results of creating a dimension for this table automatically: The Administration Tool creates a dimension containing four logical levels, counting the Grand Total and detail levels. The key of the table in source B that has the greatest number of columns mapped and contains the column mapping for the logical table key should be the level key for the detail level.
The parent of the detail level should be the logical level named for the logical column that maps to the key of the physical table in source A.
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Any attributes that are mapped to both A and B should be associated with level A. The parent of level A should be the logical level named for the logical column that maps to the key of the physical table in source C.
Any columns that are mapped to both A and C should be associated with level C. Table joins in a physical source might represent a pattern that results in a split hierarchy. For example, the Product table can join to the Flavor table and a Subtype table. This would result in two parents of the product detail level, one flavor level and one subtype level. You cannot create a dimension automatically in the following situations: If a dimension with joins and levels has already been created, Create Dimension does not appear on the right-click menu.
If the table is not yet joined to any other table, the option is not available because it is considered a fact table.
In a snowflake schema, if you use a table with only one source and create the dimension automatically, the child tables are automatically incorporated into a hierarchy. The child tables form intermediate levels between the Grand Total level and detail level.
SQL Server Analysis Services Attribute Relationships
If more then one child table exists for a dimension table, the hierarchy is a split hierarchy. To create a dimension automatically: In the Administration Tool, open a repository. In the Business Model and Mapping layer, right-click a logical dimension table that is not associated with any dimension.
The new dimension is displayed in the Business Model and Mapping layer. Populating Logical Level Counts Automatically Estimate Levels enables administrators to automatically populate level counts for one or more dimension hierarchies. Level counts are utilized by the query engine to determine the most optimal query plan and optimizes overall system performance.
The repository must be opened in online mode and the business model must be available for queries. Then, in the Business Model and Mapping layer, you can select any of the following logical layer elements, and then execute the Estimate Levels command: Must be available for queries. If you select this object, the Administration Tool attempts to check out all objects in the business model. You should run a consistency check on dimensions to ensure that the dimension is logically sound.
A combination of business models and dimensions. You can select multiple dimensions and multiple business models individually. When run, the Estimate Levels command also launches a consistency check on the level counts as described in the following list: Checks that a level key is valid. Columns in levels have referential integrity.