Database Reference
In-Depth Information
The research literature has proposed several
original approaches for conceptual modeling in
the DW area, some based on extensions of known
conceptual formalisms (e.g. E/R, UML), some
based on ad hoc ones. Remarkably, a comparison
of the different models pointed out that, abstracting
from their graphical form, the core expressivity
is similar, thus proving that the academic com-
munity has reached an informal agreement on the
required expressivity.
On the other hand, the proposed solutions are
not always coupled with an appropriate technique
for requirement analysis to form a methodologi-
cal approach ensuring that the resulting database
will be well-documented and will fully satisfy the
user requirements. DW specificities make these
two steps even more related than in traditional
database systems; in fact the lack of settled user
requirements and the existence of operational data
sources that fix the set of available information
make it hard to develop appropriate multidimen-
sional schemata that, on the one hand, fulfill user
requirements and on the other, can be fed from
the operational data sources.
This paper proposes a survey of the literature
related to these design steps in order to help the
reader make crucial choices more consciously. In
particular, after a brief description of the DW life-
cycle, the specific problems arising during require-
ment analysis and conceptual design are presented.
The approaches to requirement analysis are then
surveyed and their strengths and weaknesses are
discussed. Afterwards, the literature related to the
DW conceptual models is also surveyed and the
core expressivity of these models is discussed in
order to enable the reader to understand which are
the relevant pieces of information to be captured
during user-requirements analysis.
sign and maintenance is characterized by several
complexity factors that determined, in the early
stages of this discipline, a high percentage of
real project failures. A clear classification of the
critical factors of data warehousing projects was
already available in 1997 when three different
risk categories were identified (Demarest, 1997),
namely s
ocio-technical
i.e. related to the impact a
DW has on the decisional processes and political
equilibriums, t
echnological
i.e. related to the usage
of new and continuously evolving technologies,
and
design-related
i.e. related to the peculiarities
of this kind of systems. The awareness of the
critical nature of the problems and the experi-
ence accumulated by practitioners determined the
development of different design methodologies
and the adoption of proper life-cycles that can
increase the probability of completing the project
and fulfill the user requirements.
The choice of a correct life-cycle for the DW
must take into account the specificities of this
kind of system, that according to Giorgini et al.
(2007), are summarized as follows:
a)
DWs rely on operational databases that
represent the sources of the data.
b)
User requirements are difficult to collect
and usually change during the project.
c)
DW projects are usually huge projects: the
average time for their construction is 12 to
36 months and their average cost ranges
from 0.5 to 10 million dollars.
d)
Managers are demanding users that require
reliable results in a time compatible with
business needs.
While there is no consensus on how to address
points (a) and (b), the DW community has agreed
on an approach that cuts down costs and time to
make a satisfactory solution available to the final
users. Instead of approaching the DW develop-
ment as a whole in a top-down fashion, it is more
convenient to build it bottom-up working on single
data marts (Jensen et al., 2004). A
data mart
is
BACKGROUND
The DW is acknowledged as one of the most
complex information system modules and its de-
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