Attribute Relationships: Settings and Properties - Page 2

Procedure: Define Attribute Relationships and Examine Attribute Relationship Property Settings in Analysis Services 2005

In the practice procedures that follow, we will select and examine a representative dimension within the sample cube, and then focus upon the attribute relationship property settings that reference select dimension attributes. We will perform our practice sessions within the SQL Server Business Intelligence Development Studio, from which we will examine the attribute relationship property settings within our Analysis Services database, ANSYS065_Basic AS DB.

In Dimensional Model Components: Dimensions Part I and II, and Dimensional Attributes: Introduction and Overview Parts I through V, respectively, we overviewed the properties underpinning Database and Cube dimensions, and then examined the properties supporting dimension attributes. In Attribute Member Keys – Pt I: Introduction and Simple Keys, and in Attribute Member Keys – Pt II: Composite Keys we focused upon those properties for a simple attribute key and a composite attribute key, respectively. Just as we did for the respective subject matter objects in those articles, we will examine the detailed settings for representative attribute relationships here.

As I noted earlier, we can organize attribute hierarchies into levels within user hierarchies to provide navigation paths for users in a cube. A user hierarchy can represent a natural hierarchy, such as city, state, and country, (as we shall see in our practice session) or can simply represent a navigation path that fits a local business scenario, such as employee name, title, and department name. Moreover, as we also mentioned earlier, to the information consumer navigating a hierarchy, these two types of user hierarchies are identical.

As a part of my discussion in Introduction to Attribute Relationships in MSSQL Server Analysis Services, I stated that, with a natural hierarchy, if we define attribute relationships between the attributes that make up the levels, Analysis Services can use an aggregation from one attribute to obtain the results from a related attribute. If there are no defined relationships between attributes, Analysis Services will aggregate all non-key attributes from the key attribute.

Let’s get some hands-on exposure with defining attribute relationships for the attributes in a natural user hierarchy that exists within our basic UDM. Within our practice session we will work within the Customer Geography (natural) hierarchy of the Customer dimension.

Define Attribute Relationships for Attributes in the Customer Geography Hierarchy

We will begin our practice with attribute relationships within the Customer Geography hierarchy of the Customer dimension.

The tabs of the Dimension Designer open.

The attributes belonging to the Customer dimension appear as depicted in Illustration 4.

We note that twenty attributes appear within the Attributes pane. We will get some exposure to attribute relationships, by adding / examining representative relationships among the attributes we see here.

We can also see, within the Hierarchies and Levels pane, four levels in the Customer Geography user hierarchy. This hierarchy currently exists simply as a drill down path for information consumers, and appears as shown in Illustration 5.

We note that four member properties link the non-key attributes from the Geography table to the key attribute from the Geography table, as depicted in Illustration 6.

We see that the Geography attribute is related to the Full Name attribute. We also note that the Postal Code attribute is indirectly related to the Full Name attribute through the Geography attribute, because Postal Code is linked to the Geography attribute and the Geography attribute is linked to the Full Name attribute. These relationships are circled in Illustration 7.

The Postal Code attribute is now directly related to the Full Name attribute.

In the Properties window (which appears for the highlighted Postal Code attribute, by default in the right bottom corner of the design environment), we can observe that the RelationshipType property for this attribute is set to Flexible. This is appropriate because the relationship between a customer and a postal code may change over time.

The RelationshipType property defines rules for the modification of the key value of the members of the related, dependent attribute (in our example, the Full Name attribute is the current attribute, whereas the Postal Code attribute is the related attribute). When we define an attribute relationship, we use the RelationshipType property to specify that the relationship is one of the following types:

• Rigid: The key values of the related attribute and the current attribute have a fixed association, and cannot change without a full reprocessing of the dimension.
• Flexible: The key of the related, dependent attribute, and therefore the entire member of the dependent attribute, can be changed anytime. In our example above, the Postal Code attribute is dependent upon the Full Name (Customer) attribute with a Flexible relationship, since the postal code can change anytime a customer moves to another location.

If we define a relationship as Rigid, Analysis Services retains aggregations when the dimension is updated. If a relationship that is defined as Rigid actually changes, Analysis Services generates an error during processing unless the dimension is fully processed. Specifying the appropriate relationships and relationship properties increases query and processing performance, as we noted in Introduction to Attribute Relationships in MSSQL Server Analysis Services.

We noted that the RelationshipType property in our example is set to Flexible: The key of the related, dependent attribute, and therefore the entire member of the dependent attribute, can be changed anytime. In our example above, the Postal Code attribute is dependent upon the Full Name (Customer) attribute with a Flexible relationship, since the postal code can change anytime a customer moves.

While we will not make modifications here, we also see that the Cardinality setting for the Postal Code attribute relationship is set to Many. Cardinality defines the nature of the relationship of the key of related attributes (and their members) when those members are used as member properties of the current attribute (and its members). The Cardinality setting can have one of two possible values:

• One (One – to – One): One, and only one, member of the current attribute is associated with each member of the related attribute. For example, if we were associating the full names of customers with a social security, or other “unique” identifying code (this attribute does not exist in the example UDM – I am only using it as an illustration here), we would have a one – to – one relationship.
• Many (One – to – Many): A given member of a related attribute can be associated with multiple members of the current attribute. Needless to say, one – to – many relationships occur far more often in Analysis Services than one – to – one relationships.

Finally, we can see that the Visible setting for the Postal Code attribute relationship is set to True. The Visible setting specifies whether the related attribute is accessible, as a member property of the current member, to the information consumer. The Visible setting can have either of two possible values:

• False: The related attribute is not visible to the information consumer, and therefore cannot be used as a member property of the current member.
• True: The related attribute is visible to, and can be accessed by, the information consumer as a member property of the current member.

For purposes of our immediate example, we will leave the Visible property as its current setting of True. The Properties window for the Postal Code attribute relationship appears as depicted in Illustration 9.

The City attribute is currently related to the Postal Code attribute through the Geography attribute, rather than being directly related. We will next directly relate the City attribute to the Postal Code attribute.

The City attribute is now directly related to the Postal Code attribute. In the Properties window, we note, as we did for the Postal Code attribute earlier, that the RelationshipType property for this attribute is set to Flexible. This is appropriate because the relationship between a city and a postal code may change over time.

The State-Province attribute is currently (indirectly) related to the City attribute through the Full Name and Geography attributes, hence we do not presently see the State-Province attribute under City.

The value of the RelationshipType property for the State-Province attribute relationship should be set to Rigid because the relationship between a city and a state does not change over time.

The value of the RelationshipType property of the Country Region attribute relationship should be set to Rigid because the relationship between a state-province and a country-region does not change over time.

Because we have, in the foregoing steps, moved all the attribute relationships from the Geography attribute to other attributes, instead of creating new attribute relationships for each of those attributes, there is no reason for retaining the now-empty Geography attribute.

This will conclude the first half of our work with attribute relationships. We will continue our practice with these important relationships within the next article of this series.

Processing begins, and we see completion of the various steps via the Process Progress viewer. We see a “Process Succeeded” message in the Status bar at the bottom of the viewer, once processing is complete, as depicted in Illustration 13.

Conclusion

In this article, we continued our exploration of attribute relationships, stating that our objective would be to complement the introduction we undertook in Introduction to Attribute Relationships in MSSQL Server Analysis Services, through a more detailed examination of attribute relationships. Our concentration upon these details was enhanced by a hands-on practice session, where we gained exposure to the properties and settings that underlie attribute relationships.

Our examination included a review of the nature of the attribute relationship in Analysis Services, and its possible roles in helping to meet the primary objectives of business intelligence, based upon and extending the discussion we initiated in Introduction to Attribute Relationships in MSSQL Server Analysis Services. We performed a detailed examination of the properties underlying attribute relationships, along with a review of the respective settings associated with each property, based upon a representative dimension attribute within our sample UDM, as a part of our practice session. (We stated that we would gain further hands-on practice, working with attribute relationships within additional dimensions, in the article that follows this one.) Throughout our practice procedures we obtained hands-on exposure to creating, modifying and deleting attribute relationships within a select dimension attribute within our sample UDM.

About the MSSQL Server Analysis Services Series

This article is a member of the series Introduction to MSSQL Server Analysis Services. The series is designed to provide hands-on application of the fundamentals of MS SQL Server Analysis Services (“Analysis Services”), with each installment progressively presenting features and techniques designed to meet specific real-world needs. For more information on the series, please see my initial article, Creating Our First Cube. For the software components, samples and tools needed to complete the hands-on portions of this article, see Usage-Based Optimization in Analysis Services 2005, another article within this series.

» See All Articles by Columnist William E. Pearson, III

Discuss this article in the MSSQL Server 2000 Analysis Services and MDX Topics Forum.