![](bg8.png)
Furthermore, the results indicate that the application
within the knowledge layer can be placed in either
the vehicle or the backend, leaving computational re-
sources and data transmission costs as the primary
factors impacting the decision of its deployment site.
In future work, we intend to evaluate the approach
with more use cases by adding only ontologies and
rules to show the scalability of the proposed archi-
tecture and to look into best practises. Additionally,
we plan to extend the knowledge layer by including
machine learning techniques for obtaining further in-
sights from vehicle-related data and investigating how
machine learning and reasoning can work together.
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