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
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
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
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:
of relevant and accurate information representing business operations and
the rapid and
accurate return of query results;
dice query creation and modification;
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
introduction to dimension attributes from a conceptual perspective;
the general characteristics of attributes;
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
hierarchy. (We will examine user hierarchies, to which we will simply
refer as hierarchies, in a subsequent article.)
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
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.
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.
-> Open from the main menu.
Services Database ... from the cascading menu, as depicted in Illustration
Illustration 1: Opening
the Analysis Services Database ...
to Database dialog appears.
the Connect to existing database radio button is selected, type the Analysis
Server name into the Server input box atop the dialog.
selector just beneath, labeled Database, select ANSYS065_Basic AS DB,
as shown in Illustration
Selecting the New Basic Analysis Services Database ...
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.