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Table 1: Tableau.
client city region country dimension
M.ROSSI MODENA ER ITALY d1
P.BIANCHI FLORENCE TO ITALY d1
A.RENZO BOLOGNA ER ITALY d1
A.MANCINO MODENA v1 ITALY d2
S.RUSSO FLORENCE v2 ITALY d2
T.CONTI ROME v3 ITALY d2
5 CONCLUSIONS
The work presented in this paper describes a method
for the integration of heterogeneous Data Warehouse
dimensions. The main conclusion that can be drawn
from the paper is that the particular multidimensional
structure of DW information may be successfully ex-
ploited together with other classical data integration
approaches/techniques(like the d-chase procedure) to
achieve DW integration.
The method proposed in the paper is divided into
two steps. First, topological properties are used to
generate a mapping set between various dimension
levels, then, compatible schema parts, and the infor-
mation that is populated by, are integrated. The steps
can be independently modified in order to increase the
accuracy. In fact, one area of possible future work
is to expand the mapping generating step by using
a mixture of approaches, for example by adding the
use of semantics. It has been proven in classical data-
integration (for example (Bergamaschi et al., 2007))
that a combined approach usually increases the accu-
racy of the mapping generation step.
Another challenging problem in DW integration
that we believewill be the fruit of intensiveresearch is
the final integration of multidimensional information.
Although it relies on mapping predicates, this partic-
ular step will raise some issues, like the discovery of
common information which needs to be identified in
order to maintain the final result unalterated.
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