Towards Efficient Evolving Multi-Context Systems (Preliminary Report)
  Managed Multi-Context Systems (mMCSs) provide a general framework for
integrating knowledge represented in heterogeneous KR formalisms. Recently,
evolving Multi-Context Systems (eMCSs) have been introduced as an extension of
mMCSs that add the ability to both react to, and reason in the presence of
commonly temporary dynamic observations, and evolve by incorporating new
knowledge. However, the general complexity of such an expressive formalism may
simply be too high in cases where huge amounts of information have to be
processed within a limited short amount of time, or even instantaneously. In
this paper, we investigate under which conditions eMCSs may scale in such
situations and we show that such polynomial eMCSs can be applied in a practical
use case.
