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Figure 14: User feedback on task 2.
be integrated but also of data integration technolo-
gies. The goal of our research was to enable the peo-
ple with less or no knowledge of these technologies
to integrate their data. We presented a user-centric
approach to data integration that is based on a cogni-
tive support model, which has been implemented in
the MappingAssistant data integration tool. We pre-
sented the results of a user study demonstrating that
the approach empowers users to solve data integra-
tion problems more effectively and efficiently. In par-
ticular, we showed that users were able to find more
errors in mapping rules in a given period of time.
Further, we were able to show that while with con-
ventional mapping technology a high level of exper-
tise in mapping technology is required, the Mappin-
gAssistant approach significantly reduces the perfor-
mance difference of experienced and inexperienced
users. Finally, we could show that users considered
our approach to be more intuitive.
We believe that the user-centric approach to data
integration presented in this paper can have a real
impact on the practice of data integration in enter-
prises. In particular, the approach can help expert
users within an organization to retain more responsi-
bility for data integration. While today, data integra-
tion tasks often either have to be delegated to the IT
department or even be outsourced to specialized com-
panies, our technology can enable users to perform
non critical data integration tasks themselves. This
can save time and money in enterprise data integra-
tion. Furthermore, it can create more options for on
the fly data integration or mesh-ups that can provide
useful information but are not needed on a regular ba-
sis.
In future work we will extend the presented di-
agnosis component of the MappingAssistant with an
induction component. This induction component will
provide suggestions for repairing the wrong mapping
rule which has previously been found with the diag-
nosis component.
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