Never-stop Learning: continuous validation of learned models for evolving systems through monitoring

摘要

Interoperability among the multitude of heterogeneous and evolving networked systems made available as black boxes remains a tough challenge. Learning technology is increasingly employed to extract behavioural models that form the basis for systems of systems integration. However, as networked systems evolve, their learned models need to evolve as well. This can be achieved by collecting actual interactions via monitoring and using these observations to continuously refine the learned behavioural models and, in turn, the overall system. This approach is part of the overall CONNECT approach.