Dimension Attributes: Introduction and Overview, Part III

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.

In Dimension Attributes:
Introduction and Overview, Part II, the second of a multi-part article introducing dimension
attributes, we continued our current subseries focusing upon dimensional
model components, with an objective of discussing the associated concepts,
and of providing hands-on exposure to the properties supporting each.
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.

Having covered the general characteristics and purposes of attributes,
we fixed our focus upon the properties underlying them, based upon the
examination of a representative attribute within our sample cube. We
discussed the Basic group of properties, looking forward to subsequent
parts of our introduction and overview of dimension attributes, where we explore the remaining attribute
properties groups. In this, the second part of our overview of attributes,
our examination will
include:

• A continuation
  of our introduction to dimension attributes from a conceptual
  perspective;
• Extended
  discussion surrounding the general characteristics of attributes;
• An examination
  of the Misc group of attribute properties (including what these properties
  define and support, and how we can manage them).

Dimensions in Analysis Services: Attributes (continued …)

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 these 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 most
of our articles, the term “attribute” means the same thing as “attribute
hierarchy”. (We will examine user hierarchies, to which we will simply
refer as “hierarchies,” in subsequent articles.)

As we
noted in Dimension Attributes: Introduction and Overview, Part II, we might summarize our
introduction in Dimension Attributes: Introduction and Overview, Part I by stating that we might extend
the metaphor we used in Part I in describing dimensions as nouns and measures
as verbs, and 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.

As we
discussed in Part I, 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 conceptual hierarchies of this sort are
common in real-world dimensions.

As we
further discussed in Part I, 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 looking forward to our practice sessions in Part I and Part II, we stated that, 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 got some hands-on exposure to some of these key
values and properties in the practice session – focusing upon
the Advanced properties group of attribute properties
(including what they define and support, and how we can manage them). We will
examine, in like manner, the Misc, Parent-Child and Source
groups of attribute properties in the practice section of this (where we
focus upon the Misc properties) and subsequent articles. 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 continue our
examination of attributes within the same practice environment, which we
will access taking the following steps within the SQL Server Business
Intelligence Development Studio, as we did within Dimensional Model
Components: Dimensions Part II.

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 in
previous articles, we will continue to get some hands-on exposure to properties
for an example attribute, from within our sample UDM.