Paul Pao-Yen Wu

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This paper presents a novel framework for the modelling of passenger facilitation in a complex environment. The research is motivated by the challenges in the airport complex system, where there are multiple stakeholders, differing operational objectives and complex interactions and interdependencies between different parts of the airport system.(More)
Large networks of queueing systems model important real-world systems such as MapReduce clusters, web-servers, hospitals, call-centers and airport passenger terminals. To model such systems accurately we must infer queueing parameters from data. Unfortunately, for many queueing networks there is no clear way to proceed with parameter inference from data.(More)
The application of machine learning and statistical predictive models to business problems has found success in recent years due to an exponential increase in consumer data and readily available computational power. However, visualising and interpreting this data to support business decision making in the context of consulting businesses is challenging and(More)
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