Reasoning at Runtime using time-distorted Contexts. A based Approach

Intelligent systems continuously analyze their context
to autonomously take corrective actions. Building a proper
knowledge representation of the context is the key to take adequate
actions. This requires numerous and complex data models,
for example formalized as ontologies or meta-models. As these
systems evolve in a dynamic context, reasoning processes typically
need to analyze and compare the current context with its history.
A common approach consists in a temporal discretization, which
regularly samples the context (snapshots) at specific timestamps
to keep track of the history. Reasoning processes would then
need to mine a huge amount of data, extract a relevant view,
and finally analyze it. This would require lots of computational
power and be time-consuming, conflicting with the near real-time
response time requirements of intelligent systems. This paper
introduces a novel temporal modeling approach together with
a time-relative navigation between context concepts to overcome
this limitation. Similarly to time distortion theory, our approach
enables building time-distorted views of a context, composed
by elements coming from different times, which speeds up the
reasoning. We demonstrate the efficiency of our approach with
a smart grid load prediction reasoning engine.