Faster Bayesian Context Inference by Using Dynamic Value Ranges

Abstract

This paper shows how to reduce evaluation time for context inference. Probabilistic Context Inference has proven to be a good representation of the physical reality with uncertain or missing information, giving with the probability also a measure of the quality of information. As the inference complexity is very high, the complexity of the to be evaluated rule (representing a share of the real world) should be reduced as far as possible. Therefore we present an approach to select only relevant values of context types and to adapt this selection during its usage time. A short proof of concept indicates that both targets, reducing inference time and maintaining quality of information, can be reached with the proposed approach.