Databases Reference
In-Depth Information
design, quality-related aspects have not been explicitly incorporated [16].
Although DB research and practice have not been focused traditionally on
quality-related subjects, many of the developed tools and techniques (integ-
rity constraints, normalization theory, transaction management) have influ-
enced data quality. It is time to consider information quality as a main goal
to achieve, instead of a subproduct of DB creation and development
processes.
Most of the works for the evaluation of both data quality and data
model quality propose only lists of criteria or desirable properties without
providing any quantitative measures. The development of the properties is
usually based upon experience in practice, intuitive analysis, and reviews of
relevant literature. Quality criteria are not enough on their own to ensure
quality in practice, because different people will generally have different
interpretations of the same concept. According to the total quality manage-
ment (TQM) literature, measurable criteria for assessing quality are necessary
to avoid arguments of style [17]. Measurement is also fundamental to
the application of statistical process control, one of the key techniques of
the TQM approach [8]. The objective should be to replace intuitive notions
of design quality with formal, quantitative measures to reduce subjectivity
and bias in the evaluation process. However, defining reliable and objective
measures of quality in software development is a difficult task.
This chapter is an overview of the main issues relating to the assessment
of DB quality. It addresses data model quality and also considers data (val-
ues) quality.
14.2
Data Model Quality
A data model is a collection of concepts that can be used to describe a set of
data and operations to manipulate the data. There are two types of data mod-
els: conceptual data models (e.g., E/R model), which are used in DB design,
and logical models (e.g., relational, hierarchy, and network models), which
are supported by DBMSs. Using conceptual models, one can build a descrip-
tion of reality that would be easy to understand and interpret. Logical mod-
els support data descriptions that can be processed by a computer through a
DBMS. In the design of DBs, we use conceptual models first to produce
a high-level description of the reality, then we translate the conceptual model
into a logical model.
Although the data modeling phase represents only a small portion
of the overall development effort, its impact on the final result is probably
