Attribute Member Keys - Pt II: Composite Keys

September 19, 2008

About the 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.

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 questions quickly and easily, and achieve rapid results datasets.

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 earlier 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 learn in other articles of this series, two types of hierarchies exist within Analysis Services: attribute hierarchies and user (sometimes called “multi-level”) hierarchies. For purposes of this 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.

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 a representative attribute within our sample cube. In this article, we will continue our extended examination of attributes to yet another important component we have touched upon earlier, the attribute member key, with which we will continue to get some hands-on exposure 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.

In Part I of this article, we introduced Attribute Member Keys, 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 reviewed our initial introduction to the dimensional model and summarized its role in meeting the primary objectives of business intelligence. Next, we provided a brief overview of dimension attributes in general, referencing a subseries of articles, within my Introduction to MSSQL Server Analysis Services series, where we explore the properties underlying them in detail.

As a part of our exploration of Attribute Member Keys, 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 develop throughout Parts I and II of this article), discussing its role in meeting our business intelligence needs. We next followed with a discussion of the role of the Key attribute from a technical perspective, including its purpose within a containing dimension within Analysis Services.

We then introduced the concepts of simple and composite keys, narrowing our exploration in Part I to the former. We reviewed the Properties associated with a simple key, based upon the examination of a representative dimension attribute, Geography, within our sample UDM. Finally, we looked ahead to this, the second half of the article, where we will explore the Properties associated with a composite key, and gain hands-on exposure within a sample cube. Our examination will include:

  • A review of the three attribute usage types that we can define within each containing dimension.
  • A review of the nature of the attribute member key and its role in meeting the primary objectives of business intelligence.
  • A review of the role of the attribute member key from a technical perspective, including its purpose within its containing dimension within Analysis Services.
  • A discussion of the differences between simple and composite keys, and an explanation as to why composite keys are sometimes required to uniquely identify attribute members.
  • A review of the Properties associated with a composite key, based upon the examination of a representative dimension attribute within our sample UDM.







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