Dimension Attributes: Introduction and Overview, Part IV

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
(“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
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 I
, the third of our 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 Misc 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 part of our overview of attributes, our examination will include:

  • A continuation
    of our introduction to dimension attributes from a conceptual
  • Extended discussion
    surrounding the general characteristics of attributes;
  • An examination
    of the Parent-Child 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

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 earlier articles
of this subseries by extending the metaphor we have used 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
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 each of our practice sessions in Parts I, II and III, 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,
Basic and Misc properties groups, respectively, of attribute
properties (including what they define and support, and how we can
manage them). We will examine, in like manner, the Parent-Child and Source
groups of attribute properties in the practice section of this (where we
focus upon the Parent-Child properties) and the subsequent article.

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 Parts I, II, and III

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.

Procedure: Examine Further Attribute Properties in Analysis Services 2005

Having begun an examination of the
properties that define and support a representative attribute in Dimension
Attributes: Introduction and Overview, Parts I
, II, and III, we focused upon the Advanced, Basic
and Misc groups, respectively, of attribute
properties within our practice sessions. In the practice procedures that
follow, we will examine the properties that are classified within the Parent
– Child
group of the same attribute with which we worked in Part I,
namely Geography Key, one of the attributes belonging to the Geography

We will conduct our practice
sessions within the SQL Server Business Intelligence Development Studio,
from which we will perform our examination of attribute properties within
our Analysis Services database, ANSYS065_Basic AS DB. In Dimension Attributes: Introduction and Overview, Part I, we noted that, to
access the properties settings for
attributes within a representative dimension, we needed to open
that dimension within the Dimension Designer first. (Recall
that, because database dimensions, and not cube dimensions,
contain attributes, we access properties supporting dimension
via the Dimension Designer, and not the Cube Designer.)

Within the Solution
, right-click the Geography dimension (expand the Dimensions
folder as necessary).

Click Open
on the context menu that appears, as depicted in Illustration 3.

3: Opening the Dimension via the Dimension Designer …

tabs of the Dimension Designer open.

Click the Dimension
tab, if we have not already arrived there by default.

We noted in Part
that five attributes appear
within the Attributes pane of the Dimension
tab. The attributes belonging to
the Geography dimension appear as shown in
Illustration 4

4: The Member Attributes, Geography Dimension

will continue our examination of the properties associated with attributes by re-entering the Geography
Key attribute
, as before.

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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