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2 IMPORTANT TERMS
AND SYSTEM MODEL
In this section, it is important to clarify a few critical
terms that are needed to set the stage for everything
else that follows.
The first term is entity, which is defined as a
concept in the domain of application from which a
data type is defined, where data type is an element
that represents the structure of a top-level concept
and is a template for instances of entity types in a
system.
Another important term is indicator, which is
defined as a set of data belonging to one or more
entities which are related to each other in the data
model. It’s not only necessary to be related, but also
that they have the same meaning in the
organization’s business logic.
An important aspect of the system that we will
implement based on these ideas is its generic and
configurable structure, independent of the
application domain and requiring only a set of
rigorously defined terms in order to allow navigation
through all information.
Thus, the aim is to develop a web-based system
that allows structured access to information,
according to the organization’s business logic. This
gives to the end user the opportunity to do queries
using a configurable and appropriate interface. The
integrity of these queries will be guaranteed through
the modeling process required by ORM (Object/
Relational Mapping), because this, ORM, will
guarantee that the end user has restrictions on data
access, avoiding mistakes, unlike conventional
systems in which the ORM is applied on developer
side.
Based on this, the system is built around the
approach of the Entity Framework (EF) from
Microsoft, ADO.NET. There are other ORM
technologies available in the market, but we choose
this one because it comes from a major software
supplier, is the ORM embedded in.NET Framework,
and is the approach that we are studying.
It is also important to mention that changes in the
data model shouldn’t force the rewrite of the whole
system and should allow the reuse of all available
information.
The idea is to provide to end users a list of
possible graphic representation forms for an
indicator, because the aim is to constrain possible
actions by the user, thereby reducing the probability
of mistakes on selecting a graphical representation.
We select that kind of representation, although
there are several ways to display and process these
types of data, but the most simple is through
graphical representation, being this conclusion given
by (Tufte, 2001) “(…) graphics are instruments for
reasoning about quantitative information. Often the
most effective way to describe, explore, and
summarize a set of numbers – even a very large set –
is to look at pictures of those numbers. Furthermore,
of all methods for analyzing and communicating
statistical information, well-designed data graphics
are usually the simplest and at the same time the
most powerful”.
These systems, which help decision-making,
must have the simplest forms of data representation,
i.e., it should be able to easily illustrate the graphical
representation of sums, dates and tables.
It is also expected to illustrate facts that have
occurred in time, i.e., present time series, for a given
data, because with only one dimension this kind of
representation allows to display time series, with an
appropriate scale that can’t be achieved with another
type of graphical representation.
Another important feature is the presentation of
indicators regarding space, because organizations
typically have data that is related to locations /
regions and are of great importance to the
organization’s business model.
The graphical presentation is one of the key
points in this system, because according to (Tufte,
2001) “(…) No doubt some graphics do distort the
underlying data, making it hard for the viewer to
learn the truth”. Thus, it is important that the system
can filter out these types of representations,
according to the types of data that compose the
indicator; this feature will be described in section 3
of this paper.
The system that we are implementing is clearly
placed in the scope of the so-called OLAP, i.e On-
Line Analytical Processing.
OLAP provides the ability to analyse different
aspects of information in a fast and dynamic way,
where large volume of data are contained within a
data warehouse according to (Thomsen, 2002) “(…)
OLAP is meant to contrast with OLTP (On-Line
Transaction Processing). The key aspects are that
OLAP is analysis-based and decision-oriented”.
Until recently these systems were known as DSS
(Decision Support Systems), but now it is common
to refer to them as Business Intelligence (BI)
systems, which according to (Rud, 2009), “(…) BI is
defined as the ability for an organization to take all
its capabilities and convert them into knowledge
(…)”.
The concept of BI is relatively recent and BI
systems are usually composed of a set of tools that
enable report generation and allow users to extract
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