Analyzing and Mining Large Uncertain Data Sets: models, methods and applications

不确定性数据在一些重要的应用中是固有的，例如使用传感器网络的环境监测，使用客户调查的市场分析，以及定量经济学研究。这些应用中的不确定性数据通常由数据的随机性和不完整性、测量设备的局限性、数据更新延迟等因素引起。例如，传感器通常用于检测偏远或保护地区存在的濒危、受威胁或特别关注风险类别的动物。由于传感器的限制，检测不可能一直准确。相反，检测置信度通常用来估计模型的不确定性。分析和挖掘从这些传感器收集的不确定数据可以使生物学家捕捉和理解野生动物有趣的行为模式。

Uncertain data are inherent in some important applications, such as environmental surveillance using sensor netoworks, market analysis using customer surveys, and quantitative economics research. Uncertain data in those applications are generally caused by factors like data randomness and incompleteness, limitations of measuring equipment, delayed data updates, etc. For example, sensors are often used to detect presence of endangered, threatened, or special concern risk categories of animals in remote or preserved regions. Due to limitations of sensors, detections cannot be accurate all the time. Instead, detection confidence is often estimated to model uncertainty. Analyzing and mining uncertain data collected from such sensors can enable biologists to capture and understand interesting behavior patterns of wild animals.