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.

Introduction

In Dimensional Model Components: Dimensions Parts I and II, we introduced the dimensional model in general, noting its wide acceptance as the preferred structure for presenting quantitative and other organizational data to information consumers. We then began our 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 quick and easy questions, and achieve rapid results datasets.

In this, the third of several articles focusing upon dimensional model component structures as they are implemented within Analysis Services 2005, we will introduce attributes, another key component. Our examination will include:

• An introduction to dimension attributes from a conceptual perspective;
• Coverage of the general characteristics of attributes;
• An examination of the Advanced properties (including what they define and support, and how we can manage them) underpinning attributes;
• A look ahead to Part II of this article, where we explore the Basic, Misc, Parent-Child and Source groups of attribute properties.

Dimensions in Analysis Services: Attributes

We learned, in Dimensional Model Components: Dimensions Parts I and II that dimensions form the foundation of the dimensional model. They represent the perspectives of a business or other operation, and reflect the intuitive ways that information consumers need to query and view data. We noted that we might consider dimensions as nouns that take part in, or are otherwise associated with, the verbs (or actions / transactions undertaken by the business) that are represented by the facts or measures contained within our business intelligence systems.

We discovered in the earlier two articles that, within the Analysis Services model, database dimensions underlie all other dimensions, whose added properties distinguish them from the database dimensions they reference, within the model. Each dimension within our model contains one or more hierarchies. As we will learn in later articles of this subseries, two types of hierarchies exist within Analysis Services: attribute hierarchies and user (sometimes called “multi-level”) hierarchies. For purposes of our article, the term “attribute” means the same thing as “attribute hierarchy”. (We will examine user hierarchies, to which we will simply refer as “hierarchies,” in a subsequent article.)

To extend the metaphor we used earlier in describing dimensions as nouns and measures as verbs, we might consider attributes as somewhat similar to adjectives. That is, attributes help us to define with specificity what dimensions cannot define by themselves. Dimensions alone are like lines in geometry: they don't define “area” within multidimensional space, nor do they themselves even define the hierarchies that they contain. A database dimension is a collection of related objects called attributes, which we use to specify the coordinates required to define cube space.

Within the table underlying a given dimension (assuming a more-or-less typical star schema database) are individual rows supporting each of the members of the associated dimension. Each row contains the set of attributes that identify, describe, and otherwise define and classify the member upon whose row they reside. For instance, a member of the Patient dimension, within the Analysis Services implementation for a healthcare provider, might contain information such as patient name, patient ID, gender, age group, race, and other attributes. Some of these attributes might relate to each other hierarchically, and, as we shall see in subsequent articles of this subseries (as well as within other of my articles), multiple hierarchies of this sort are common in real-world dimensions.

Dimensions and dimension attributes should support the way that management and information consumers of a given organization describe the events and results of its business operations. 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.

In addition to a few key values, several properties (each of which has, in its own right, multiple possible values) are associated with each attribute residing in a given model. We will get some hands-on exposure to these key values and properties in the practice session below. 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 UDM 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 will perform our examination of attributes within the same practice environment, which we will access using the following steps within the SQL Server Business Intelligence Development Studio, as we did within Dimensional Model Components: Dimensions Part I.

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.

The Connect to Database dialog appears.

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 dimension attributes, we will get some hands-on exposure to properties for an example attribute, from within our sample UDM.