Triggered bandits within streaming data settings.

在流数据设置中触发强盗。

• 批准号: 2753522
• 金额: --
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2753522
• 关键词: Triggered; bandits; within; streaming; data

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.

该项目的主要目标是开发新的决策算法，以结合当前的流数据异常检测技术。该项目与英国电信合作；英国电信是一家大型跨国电信提供商，仅在英国境内就管理着约2800万条电话线，并为关键的国家电信基础设施的其他领域提供维护。通过BT网络收集范围广泛的重要电信数据，并由BT监控。已开发了对流数据的异常检测方法；这些方法可应用于电信数据。电信数据中的异常有时是关键事件的后果；因此，在BT中检测到异常后迅速做出最佳决策对于确保维护关键的国家基础设施非常重要。我们将开发的新型决策算法将是自我优化和自适应的。此外，该算法还将反馈给异常检测方法，以提高检测的准确性和时延。本项目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？异常现象是如何分类的？该项目有一个数学科学研究主题，重点是统计学、运筹学和人工智能技术领域。