Abstract
Intelligent systems continuously analyze their context to
autonomously take actions. Building a proper knowledge representation
of the context is key to take adequate actions.
This requires context models, e.g. formalized as ontologies or
meta-models. As these systems evolve in dynamic contexts,
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 at specific timestamps (snapshots) to keep track
of history. Fig. 1 shows a context sampled at three different
timestamps. Reasoning processes would then need to mine
a huge amount of data, extract a relevant view, and finally
analyze it. This would require lot of computational power
and be time-consuming, conflicting with the near real-time
response time requirements of intelligent systems. To address
these issues, we define time-distorted contexts as time-aware
context models. Fig. 2 shows a context representation, where
the context variables belong to different timestamps. Our
approach considers temporal information as first-class property
crosscutting any context element, and enables building timedistorted
views of a context composed by elements from
different times rather than a mere stack of snapshots. We
claim that these time-distorted views can efficiently empower
continuous reasoning processes and outperform traditional full
sampling approaches by far.
autonomously take actions. Building a proper knowledge representation
of the context is key to take adequate actions.
This requires context models, e.g. formalized as ontologies or
meta-models. As these systems evolve in dynamic contexts,
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 at specific timestamps (snapshots) to keep track
of history. Fig. 1 shows a context sampled at three different
timestamps. Reasoning processes would then need to mine
a huge amount of data, extract a relevant view, and finally
analyze it. This would require lot of computational power
and be time-consuming, conflicting with the near real-time
response time requirements of intelligent systems. To address
these issues, we define time-distorted contexts as time-aware
context models. Fig. 2 shows a context representation, where
the context variables belong to different timestamps. Our
approach considers temporal information as first-class property
crosscutting any context element, and enables building timedistorted
views of a context composed by elements from
different times rather than a mere stack of snapshots. We
claim that these time-distorted views can efficiently empower
continuous reasoning processes and outperform traditional full
sampling approaches by far.
