Deep Data Mining for Anomaly Prediction from Sensor Data Streams

通过深度数据挖掘从传感器数据流进行异常预测

• 批准号: DP140100841
• 负责人: Prof Yanchun Zhang
• 金额: $ 23万
• 依托单位: Victoria University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2014
• 资助国家: 澳大利亚
• 起止时间: 2014-01-01 至 2018-08-30
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP140100841
• 关键词: Deep; Data; Mining; Anomaly; Prediction

Sensor data streams are crucial for anomaly predictions in real-life monitoring. However, balancing efficiency and accuracy in predicting anomalies with sensor streams is a great challenge; it requires new techniques that go beyond detecting anomalies and predicting trends. This project will develop a deep mining method for anomaly prediction from sensor streams; it will comprise mining algorithms at various levels - from compressing massive raw data, to recognition of abnormal waveforms preceding anomalies, and to retrieving and summarising similar past anomalies for creating descriptions of future anomalies. The project will demonstrate our method in health/environment monitoring applications, and its adoption will save resources, money and lives.

传感器数据流对于实际监测中的异常预测至关重要。然而，平衡传感器流预测异常的效率和准确性是一个巨大的挑战;它需要超越检测异常和预测趋势的新技术。该项目将开发一种深度挖掘方法，用于从传感器流中进行异常预测;它将包括各级挖掘算法-从压缩大量原始数据到识别异常之前的异常波形，以及检索和总结类似的过去异常，以创建对未来异常的描述。该项目将在健康/环境监测应用中展示我们的方法，它的采用将节省资源，金钱和生命。