Dimension Attributes: Introduction and Overview, Part III

About the Series …

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
”), 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
Optimization in Analysis Services 2005
, another article within this


In Dimensional
Model Components: Dimensions Parts I
and II, we introduced the dimensional
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
  • 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
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
groups. In this, the second part of our overview of attributes,
our examination will

  • A continuation
    of our introduction to dimension attributes from a conceptual
  • 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 …)

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.

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
”. (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
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
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

NOTE: Please access the UDM
which we prepared in Dimensional Model Components: Dimensions Part I before proceeding with this
article. If you have not completed the preparation to which I refer in the
previous article, or if you cannot locate / access the Analysis Services
database with which we worked there, 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

Click Start.

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

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

Close the Start
page, if desired.

Select File
> Open from the main menu.

Click Analysis
Services Database …
from the cascading menu, as depicted in Illustration

Illustration 1: Opening
the Analysis Services Database …

The Connect
to Database
dialog appears.

Ensuring that
the Connect to existing database radio button is selected, type the Analysis
name into the Server input box atop the dialog.

Using the
selector just beneath, labeled Database, select ANSYS065_Basic AS DB,
as shown in Illustration

Illustration 2:
Selecting the New Basic Analysis Services Database …

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

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.

William Pearson
William Pearson
Bill has been working with computers since before becoming a "big eight" CPA, after which he carried his growing information systems knowledge into management accounting, internal auditing, and various capacities of controllership. Bill entered the world of databases and financial systems when he became a consultant for CODA-Financials, a U.K. - based software company that hired only CPA's as application consultants to implement and maintain its integrated financial database - one of the most conceptually powerful, even in his current assessment, to have emerged. At CODA Bill deployed financial databases and business intelligence systems for many global clients. Working with SQL Server, Oracle, Sybase and Informix, and focusing on MSSQL Server, Bill created Island Technologies Inc. in 1997, and has developed a large and diverse customer base over the years since. Bill's background as a CPA, Internal Auditor and Management Accountant enable him to provide value to clients as a liaison between Accounting / Finance and Information Services. Moreover, as a Certified Information Technology Professional (CITP) - a Certified Public Accountant recognized for his or her unique ability to provide business insight by leveraging knowledge of information relationships and supporting technologies - Bill offers his clients the CPA's perspective and ability to understand the complicated business implications and risks associated with technology. From this perspective, he helps them to effectively manage information while ensuring the data's reliability, security, accessibility and relevance. Bill has implemented enterprise business intelligence systems over the years for many Fortune 500 companies, focusing his practice (since the advent of MSSQL Server 2000) upon the integrated Microsoft business intelligence solution. He leverages his years of experience with other enterprise OLAP and reporting applications (Cognos, Business Objects, Crystal, and others) in regular conversions of these once-dominant applications to the Microsoft BI stack. Bill believes it is easier to teach technical skills to people with non-technical training than vice-versa, and he constantly seeks ways to graft new technology into the Accounting and Finance arenas. Bill was awarded Microsoft SQL Server MVP in 2009. Hobbies include advanced literature studies and occasional lectures, with recent concentration upon the works of William Faulkner, Henry James, Marcel Proust, James Joyce, Honoré de Balzac, and Charles Dickens. Other long-time interests have included the exploration of generative music sourced from database architecture.

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