  Intelligent software systems continuously analyze their surrounding
environment and accordingly adapt their internal state. Depending on the
criticality index of the situation, the system should dynamically focus or
widen its analysis and reasoning scope. A naive -why have less when you can
have more- approach would consist in systematically sampling the context at a
very high rate and triggering the reasoning process regularly. This reasoning
process would then need to mine a huge amount of data, extract a relevant view,
and finally analyze this adequate view. This overall process would require some
heavy resources and/or be time-consuming, conflicting with the (near) real-time
response time requirements of intelligent systems. We claim that a continuous
and more flexible navigation into context models, in space and in time, can
significantly improve reasoning processes. This paper thus introduces a novel
modeling approach together with a navigation concept, which consider time and
locality as first-class properties crosscutting any model element, and enable
the seamless navigation of models in this space-time continuum. In particular,
we leverage a time-relative navigation (inspired by the space-time and
distortion theory [7]) able to efficiently empower continuous reasoning
processes. We integrate our approach into an open-source modeling framework and
evaluate it on a smart grid reasoning engine for electric load prediction. We
demonstrate that reasoners leveraging this distorted space-time continuum
outperform the full sampling approach, and is compatible with most of (near)
real-time requirements.
