Abstract
The cognitively enhanced complex event processing (CECEP) architecture being developed at the US Air Force is an autonomous decision support tool that reasons and learns like humans and enables enhanced agent-based decision making. It has applications in both military and civilian domains. One of the most computationally challenging aspects of CECEP is mining domain knowledge captured in cognitive domain ontologies (CDOs). Real-time agents require massively linked knowledge databases to be searched using a large set of constraints to generate intelligent decisions in run time. This study examines how to parallelize and accelerate the CDO knowledge mining algorithm by converting it into a constraint network that can be solved using a parallelized generate and test exhaustive depth first search algorithm. Our results show that 1 GPGPU can provide speed-ups of 100 times over a Xeon CPU core and almost 8 times over a Xeon Phi processor for the search algorithm.
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