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Attribute Relationships: Settings and Properties

January 22, 2009

This article continues the overview of Attribute Relationships in Analysis Services begun in Introduction to Attribute Relationships in MSSQL Server Analysis Services. Both this article and its predecessor extend the examination of the dimensional model that we began in Dimensional Model Components: Dimensions Parts I and II. After taking up various additional components of the dimensional model in subsequent articles, we performed hands-on exploration of the general characteristics and purposes of attributes in Dimensional Attributes: Introduction and Overview Parts I through V. We then fixed our focus upon the properties underlying attributes, based upon the examination of a representative attribute within our sample cube., extending our overview into attribute member Keys, Names and Values. This article continues the focus upon attribute relationships, which define the possible associations between attributes, including a discussion surrounding why these relationships are important, and how they define the properties of association that a given attribute has with other attributes. Our concentration here will be the performance of 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.

Note: For more information about my Introduction to MSSQL Server Analysis Services column in general, see the section entitled “About the MSSQL Server Analysis Services Series” that follows the conclusion of this article.

Introduction

In Introduction to Attribute Relationships in MSSQL Server Analysis Services, I summarized the articles preceding it within the current subseries surrounding a general introduction to the dimensional model. I noted the wide acceptance of the dimensional model as the preferred structure for presenting quantitative and other organizational data to information consumers. The articles of the series then undertook an examination of dimensions, the analytical “perspectives” upon which the dimensional model relies in meeting the primary objectives of business intelligence, including its capacity to support:

  • the presentation of relevant and accurate information representing business operations and events;
  • the rapid and accurate return of query results;
  • “slice and dice” query creation and modification;
  • an environment wherein information consumers can pose questions quickly and easily, and achieve rapid results datasets.

We extended our examination of dimensions into several detailed articles. These articles are comprised of Dimensional Model Components: Dimensions Parts I and II, wherein we emphasized that dimensions, which represent the perspectives of a business or other operation, and reflect the intuitive ways that information consumers need to query and view data, form the foundation of the dimensional model. We noted that each dimension within our model contains one or more hierarchies. (As we learn in other articles of this series, two types of hierarchies exist within Analysis Services: attribute hierarchies and user - sometimes called “multi-level” - hierarchies.)

We next introduced dimension attributes within the subseries, and conducted an extensive overview of their nature, properties, and detailed settings in Dimensional Attributes: Introduction and Overview Parts I through V. We noted that attributes help us to define with specificity what dimensions cannot define by themselves. Moreover, we learned that attributes are collected within a database dimension, where we can access them to help us to specify the coordinates required to define cube space.

Throughout the current subseries, I have emphasized that dimensions and dimension attributes should support the way that management and information consumers of a given organization describe the events and results of the business operations of the entity. Because we maintain dimension and related attribute information within the database underlying our Analysis Services implementation, we can support business intelligence for our clients and employers even when these details are not captured within the system where transaction processing takes place. Within the analysis and reporting capabilities we supply in this manner, dimensions and attributes are useful for aggregation, filtering, labeling, and other purposes.

Having covered the general characteristics and purposes of attributes in Dimensional Attributes: Introduction and Overview Parts I through V, we fixed our focus upon the properties underlying them, based upon the examination of representative attributes within our sample cube. We then continued our extended examination of attributes to yet another important component we had touched upon earlier, the attribute member Key, with which we gained some hands-on exposure in practice sessions that followed our coverage of the concepts. In Attribute Member Keys – Pt I: Introduction and Simple Keys and Attribute Member Keys – Pt II: Composite Keys, we introduced attribute member Keys in detail, continuing our recent group of articles focusing upon dimensional model components, with an objective of discussing the associated concepts, and of providing hands-on exposure to the properties supporting them. We first discussed the three attribute usage types that we can define within a containing dimension. We then narrowed our focus to the Key attribute usage type (a focus that we developed, as we have noted, throughout Attribute Member Keys – Pt I: Introduction and Simple Keys and Attribute Member Keys – Pt II: Composite Keys), discussing its role in meeting our business intelligence needs. We next undertook a discussion of the nature and uses of the attribute Key from a technical perspective, including its purpose within a containing dimension in Analysis Services.

In Attribute Member Keys – Pt I: Introduction and Simple Keys and Attribute Member Keys – Pt II: Composite Keys, we explored the concepts of simple and composite keys, narrowing our examination in Part I to the former, where we reviewed the Properties associated with a simple key, based upon the examination of a representative dimension attribute within our sample UDM. In Part II, we revisited the differences between simple and composite keys, and explained in more detail why composite keys are sometimes required to uniquely identify attribute members. We then reviewed the properties associated with a composite key, based upon the examination of a representative dimension attribute within our sample UDM.

In Attribute Member Names, we examined the attribute member Name property, which we had briefly introduced in Dimensional Attributes: Introduction and Overview Part V. We examined the details of the attribute member Name property, and shed some light on how attribute member Name might most appropriately be used without degrading system performance or creating other unexpected or undesirable results. Finally, we examined the “sister” attribute member Value property (which we introduced along with attribute member Name in Dimensional Attributes: Introduction and Overview Part V) in Attribute Member Values in Analysis Services. As we did in our overview of attribute member Name, we examined the details of Value. Our concentration was also similarly upon its appropriate use in providing support for the selection and delivery of enterprise data in a more focused and consumer-friendly manner, without the unwanted effects of system performance degradation, and other unexpected or undesirable results, that can accompany the uninformed use of the property.

Finally, in our last article, Introduction to Attribute Relationships in MSSQL Server Analysis Services, we introduced another part of the conceptual model, Attribute Relationships. In this overview, we discussed several best practices and design, among other, considerations involved in the use of attribute relationships. Our focus was upon the general exploitation of attribute relationships in providing support for the selection and delivery of enterprise data.

We will continue our exploration of attribute relationships in this article, where we will examine attribute relationships in a more detailed manner, similar to the way we treated the subject matter in previous articles within this subseries. We will concentrate in detail upon the properties and settings that underlie them.

Our examination will include:

  • A review of the nature of the attribute relationship, 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;
  • A detailed examination of the properties underlying attribute relationships, along with a review of the respective settings associated with each property, based upon the attributes of a representative dimension within our sample UDM;
  • Hands-on practice in creating, modifying and deleting attribute relationships for several attributes within a representative dimension of our sample UDM;
  • A look forward to the article that follows within our series, where we will continue our detailed examination of the properties underlying attribute relationships, along with a review of the respective settings associated with each property, based upon the attributes of additional representative dimensions within our sample UDM.

Attribute Relationships

As we have learned, attributes serve as the foundation for our dimensions and cubes. Moreover, in Analysis Services 2005, attributes within a dimension are always related either directly or indirectly to the key attribute. Assuming the definition of a dimension based upon a star schema, where all dimension attributes are derived from the same relational table, an attribute relationship is automatically defined between the key attribute and each non-key attribute of the dimension. Alternatively, if we assume the definition of a dimension based upon a snowflake schema, where dimension attributes are derived from multiple related tables, Analysis Services automatically defines an attribute relationship in the following manner:

  • Between the key attribute and each non-key attribute associated with columns in the main dimension table;
  • Between the key attribute and the attribute associated with the foreign key in the secondary table that links the underlying dimension tables;
  • Between the attribute associated with the foreign key in the secondary table and each non-key attribute associated with columns from the secondary table.

As we noted in Introduction to Attribute Relationships in MSSQL Server Analysis Services, there are a number of reasons to change the assigned default attribute relationships. For example, we might want to define a natural hierarchy, a custom sort order, or dimension granularity based on a non-key attribute (we focus upon these activities in other articles of this series). We might also want to performance tune the default relationships to optimize processing in general.

Relationships representing natural hierarchies are enforced by creating an attribute relationship between the attribute for a level and the attribute for the level below it. For Analysis Services, this specifies a natural relationship and potential aggregation. In the Customer dimension of the sample Adventure Works UDM, a natural hierarchy exists for the Country, State-Province, City, Postal Code, and Customer attributes. The natural hierarchy for {Country, State-Province, City, Postal Code, Customer} has been established through the addition of the following attribute relationships:

  • The Country attribute as an attribute relationship to the State-Province attribute;
  • The State-Province attribute as an attribute relationship to the City attribute;
  • The City attribute as an attribute relationship to the Postal Code attribute.

We will see construct relationships within the UDM as part of the practice session that follows.

As we have noted in other articles of this series, we can also create a user-defined hierarchy that does not represent a natural hierarchy in the data (this is called an ad hoc or reporting hierarchy), for purposes of navigating data in the cube. For example, we could create a user-defined hierarchy based on Customer {Education, Gender}. Information consumers of the data would see no difference in how the two hierarchies behave, although the natural hierarchy benefits from aggregating and indexing structures — invisible to the consumer — that account for the natural relationships in the source data.

The attribute relationship, as we have learned, defines the possible associations that exist between attributes within a given dimension. In doing so, the attribute relationship affects virtually all functions of Analysis Services. The attribute relationship defines the properties of association that exist (including whether another attribute can be accessed via the given attribute) between a given attribute and another attribute. (A given attribute is treated as a member property of the “current” attribute when it can be accessed via the “current” attribute – hence the relatively common reference to a related attribute as a “member property” in much of the documentation and other literature.)

We will gain hands - on exposure to attribute relationship properties and settings in the practice session that follows. Before we get started working within a sample cube clone, we will need to prepare the local environment for the practice session. We will take steps to accomplish this within the section that follows.

Preparation: Locate and Open the Sample Basic UDM Created Earlier

In Dimensional Model Components: Dimensions Part I, we created a sample basic Analysis Services database within which to perform the steps of the practice sessions we set out to undertake in the various articles of this subseries. Once we had ascertained that the new practice database appeared to be in place, and once we had renamed it to ANSYS065_Basic AS DB, we began our examination of dimension properties. We continued with our examination of attributes within the same practice environment, which we will now access (as we did within the earlier articles of this subseries)) by taking the following steps within the SQL Server Business Intelligence Development Studio.

NOTE: Please access the Analysis Services database which we prepared in Dimensional Model Components: Dimensions Part I (and have used in subsequent articles) before proceeding with this article. If you have not completed the preparation to which I refer, or if you cannot locate / access the Analysis Services database with which we worked in the referenced previous articles, please consider taking the preparation steps provided in Dimensional Model Components: Dimensions Part I before continuing, and prospectively saving the objects with which you work, so as to avoid the need to repeat the preparation process we have already undertaken for subsequent related articles within this subseries.

1.  Click Start.

2.  Navigate to, and click, the SQL Server Business Intelligence Development Studio, as appropriate.

We briefly see a splash page that lists the components installed on the PC, and then Visual Studio .NET 2005 opens at the Start page.

3.  Close the Start page, if desired.

4.  Select File -> Open from the main menu.

5.  Click Analysis Services Database ... from the cascading menu, as shown in Illustration 1.

Opening the Analysis Services Database
Illustration 1: Opening the Analysis Services Database ...

The Connect to Database dialog appears.

6.  Ensuring that the Connect to existing database radio button atop the dialog is selected, type the Analysis Server name into the Server input box (also near the top of the dialog).

7.  Using the selector just beneath, labeled Database, select ANSYS065_Basic AS DB, as depicted in Illustration 2.

Selecting the Basic Analysis Services Database ...
Illustration 2: Selecting the Basic Analysis Services Database ...

8.  Leaving other settings on the dialog at default, click OK.

SQL Server Business Intelligence Development Studio briefly reads the database from the Analysis Server, and then we see the Solution Explorer populated with the database objects. Having overviewed the properties of dimension attributes in previous articles, we will now get some hands-on exposure to attribute relationships for attributes of a representative dimension within our practice UDM.








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