  Managed multi-context systems (mMCSs) allow for the integration of
heterogeneous knowledge sources in a modular and very general way. They were,
however, mainly designed for static scenarios and are therefore not well-suited
for dynamic environments in which continuous reasoning over such heterogeneous
knowledge with constantly arriving streams of data is necessary. In this paper,
we introduce reactive multi-context systems (rMCSs), a framework for reactive
reasoning in the presence of heterogeneous knowledge sources and data streams.
We show that rMCSs are indeed well-suited for this purpose by illustrating how
several typical problems arising in the context of stream reasoning can be
handled using them, by showing how inconsistencies possibly occurring in the
integration of multiple knowledge sources can be handled, and by arguing that
the potential non-determinism of rMCSs can be avoided if needed using an
alternative, more skeptical well-founded semantics instead with beneficial
computational properties. We also investigate the computational complexity of
various reasoning problems related to rMCSs. Finally, we discuss related work,
and show that rMCSs do not only generalize mMCSs to dynamic settings, but also
capture/extend relevant approaches w.r.t. dynamics in knowledge representation
and stream reasoning.
