Towards the Automated Inference of Queueing Network Models From High-Precision Location Tracking Data

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Provided by: Imperial College London
Topic: Big Data
Format: PDF
Traditional methods for deriving performance models of customer flow in real-life systems are manual, time consuming and prone to human error. This paper proposes an automated four-stage data processing pipeline which takes as input raw high-precision location tracking data and which outputs a queuing network model of customer flow. The pipeline estimates both the structure of the network and the underlying interarrival and service time distributions of its component service centers. The authors evaluate their method's effectiveness and accuracy in four experimental case studies.
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