Dimension Attributes: Introduction and Overview, Part I

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 Usage-Based
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 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

  • 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

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 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
”. (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.

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

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

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 subseries.

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,
we will 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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