Towards transforming data streams into real-time knowledge

将数据流转化为实时知识

• 批准号: DE150100104
• 负责人: Dr Masud Moshtaghi
• 金额: $ 19.56万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2015
• 资助国家: 澳大利亚
• 起止时间: 2015-04-01 至 2017-10-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE150100104
• 关键词: Towards; transforming; data; streams; into

This project aims to address a key problem of interpreting and providing meaningful information in real-time from large volumes of multivariate, noisy and incomplete data in fine-scale monitoring applications. Specifically, it targets air quality monitoring within a workplace. The project aims to significantly advance the current models for online data clustering and real-time anomaly detection in streaming data. The project aims to produce computational models for the two aforementioned tasks and a complete system prototype for indoor air quality monitoring. This system has major health benefits for workers and the showcased computational models have various industrial potentials with significant socio-economic benefits to Australia.

该项目旨在解决一个关键问题，即在精细尺度监测应用中，从大量多变量、嘈杂和不完整的数据中实时解释和提供有意义的信息。具体来说，它针对的是工作场所的空气质量监测。该项目旨在显著推进当前在线数据聚类和流数据实时异常检测模型。该项目旨在为上述两项任务提供计算模型，并为室内空气质量监测提供完整的系统原型。该系统对工人的健康有重大好处，所展示的计算模型具有各种工业潜力，对澳大利亚具有重大的社会经济效益。