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Large-scale natural disasters cause external disturbances to networking infrastructure that lead to large-scale network-service disruption. To understand the impact of natural disasters to networks, it is important to localize and analyze network-service disruption after natural disasters occur. This paper studies an inference of network-service disruption caused by the real natural disaster, Hurricane Katrina. The authors perform inference using large-scale Internet measurements and human inputs. They use clustering and feature extraction to reduce data dimensionality of sensory measurements and apply semi-supervised learning to jointly use sensory measurements and human inputs for inference.
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