Abstract
Preserving cultural heritage is one of the most intricate jobs that need to be performed with a lot of caution. Transporting artworks on the other hand, i.e., getting them out of their home museums and exposing them in uncontrollable and dynamic environments, makes preservation even harder. So far, museums try to contend against the dynamic context during transportation by employing high-class transport cases. However, no attempts are made to exploit the science of data. In this paper, we make use of the ubiquity of sensors and IoT devices, combined with advanced predictive analytics in order to manage processes based on their context and to anticipate challenging run-time violations. Our approach is tested on real datasets and transport scenarios to prove its efficiency.
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