Context is fundamental for how we perceive and understand the world. Have you ever met a person you knew from a specific context in another, completely different context, e.g. your doctor in the supermarket? You recognized the face but could not put a name to them? Context filters the multitude of information around us to allow us to react in a timely manner, and usually this works, because the world exhibits regularities that allow us to make this simplification, but sometimes it does not. Both cases are interesting for research: how do we learn contexts and switch between them? How do we mentally leave a context to gain a wider understanding, e.g. to put a name to a face in an unusual context? How do we readjust our contextual framework, for instance, when we move?
The way cognitive systems use context to handle massive amounts of information retrieved through the senses and compare and process them using massive amounts of learned knowledge is one of the fundamental differences between cognitive systems and conventional computer systems. Conventional software and hardware are carefully designed to reliably support specific tasks. Novel types of computing systems, however, such as Big Data systems or Ubiquitous/Pervasive Computing systems, require new approaches. But at the same time we need to better understand these systems so as to not lose reliability and other important qualities, such as user privacy. We founded the Journal of Reliable Intelligent Environments (Springer), which focuses on these questions.