Dimension Attributes: Introduction and Overview, Part V

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.

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 Advanced group of properties in Dimension
Attributes: Introduction and Overview, Part I
, and continued with an
examination of the Basic, Misc and Parent-Child properties
in Part
II
, Part
III
, and Part
IV
,
respectively, looking forward to each subsequent part of our introduction and
overview of dimension attributes, where we explored the remaining attribute properties.
In this 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 Source group of attribute properties (including what these
    properties define and support, and how we can manage them) underpinning attributes.

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 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 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 each of our practice sessions in Parts I, II, III and IV, 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 examined
the Basic, Misc, and Parent-Child groups of attribute
properties in Parts II, III, and IV, in like manner. We will examine the Source group of
attribute properties in the practice section of this article, where we
conclude our introduction and overview of dimension attributes.

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, III, and IV.

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.

1. 
Click Start.

2. 
Navigate to,
and click, the SQL
Server Business Intelligence Development Studio
, as appropriate.

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.

3. 
Close the Start
page, if desired.

4. 
Select File
> Open from the main menu.

5. 
Click Analysis
Services Database …
from the cascading menu, as depicted in Illustration
1
.



Illustration 1: Opening
the Analysis Services Database …

The Connect
to Database
dialog appears.

6. 
Ensuring that
the Connect to existing database radio button is selected, type the Analysis
Server
name into the Server input box atop the dialog.

7. 
Using the
selector just beneath, labeled Database, select ANSYS065_Basic AS DB,
as shown in Illustration
2
.



Illustration 2:
Selecting the New Basic Analysis Services Database …

8. 
Leaving other
settings on the dialog at default, click OK.

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.

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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