Triggered bandits within streaming data settings.

该项目的主要目的是开发新的决策算法，以整合当前流数据设置中的异常检测技术。该项目与英国电信合作；英国电信是一家大型跨国电信供应商，仅在英国境内就管理着约2800万条电话线，同时为其他重要的国家电信基础设施提供维护。广泛的重要电信数据通过BT网络收集并由BT进行监控。针对流数据的异常检测方法已经开发出来；这些方法可以应用于电信数据。电信数据中的异常有时是关键事件的后果；因此，在英国电信内部发现异常后，快速做出最佳决策对于确保关键的国家基础设施的维护至关重要。我们将开发的新的决策算法将是自优化和自适应的。此外，该算法还会对异常检测方法进行反馈，以提高检测的准确性和延迟。本课题的研究问题包括但不限于：该算法如何与异常检测方法相结合？该算法对异常检测方法提供了什么反馈？异常是如何分类的？该项目以数学科学为研究主题，重点关注统计学、运筹学和人工智能技术领域。与英国电信合作。

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.