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risk mitigation and failure avoidance.
The considerations presented in this paper are a
good basis, but requesting further research. First of
all it is inevitable to develop a conversion tool for
preservation. This tool is supposed to take sensor
and contextual information as input and convert them
into a preservable format. Of course, it is necessary
to check whether the preserved data contains all rel-
evant information that was given by the input data.
The preserved data will be replayed some time later
on. To perform this action a replaying algorithm is
required. This algorithm has to be examined regard-
ing its replay accuracy and usefulness. It is important
to check whether the replayed data behaves like the
original data did. As mentioned before in Subsection
4 there are additional possibilities to work with pre-
served data in the future than just replay it. There is
also the option to use the preserved, historic data to
predict future behaviour, e.g. by performing stream
mining algorithms. Especially in the framework of
civil engineering, where the focus is often on enhanc-
ing the safety and reducing a risk by avoiding failures,
the early detection errors or uncommon behaviour is
important. This can be achieved for example by learn-
ing a model by applying machine learning techniques
on the data. It is considerable to simulate emergency
situations, e.g. the bursting or the failure of a water
dam, by altering some parameters. The task for the
developed system is to detect this anomaly as soon as
possible to raise an alarm. The question is whether
the system is able to detect such a failure in time.
ACKNOWLEDGEMENTS
The authors would like to acknowledge the funding
by the European Commission under the ICT project
“TIMBUS” (Project No. 269940, FP7-ICT-2009-6)
within the 7th Framework Programme. This work
was also supported by national funds through FCT
Fundac¸
˜
ao para a Ci
ˆ
encia e a Tecnologia, under project
PEst-OE/EEI/LA0021/2013.
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