Triggered bandits within streaming data settings.

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The main aim of this project is to develop novel decision-making algorithms to integrate with current anomaly detection techniques in the streaming data setting. This project is partnered with BT; BT are a large multi-national telecommunications provider, managing around 28 million telephone lines within the UK alone, alongside providing maintenance for other areas of crucial national telecommunication infrastructure. A wide range of important telecommunications data is collected across the BT network and is monitored by BT.Anomaly detection methods have been developed for streamed data; these methods can be applied to the telecommunications data. Anomalies within telecommunications data are sometimes consequences of critical incidents; therefore, fast optimal decision-making after anomalies have been detected within BT is important to ensure critical national infrastructure is maintained. The novel decision-making algorithms we will develop will be self-optimising and adaptive. Furthermore, the algorithm will give feedback to the anomaly detection method to improve the accuracy and delay of detection.Research questions regarding this project include, but are not limited to: How does the algorithm integrate with the anomaly detection method? What feedback does the algorithm provide to the anomaly detection method? How are anomalies classified? This project has a Mathematical Sciences research theme, focused in the areas of Statistics, Operational Research and AI technologies.In partnership with BT.