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

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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.