U4U-Taming Uncertainty with Uncertainty-Annotated Databases Division: CISE/IIS/III

Figure 1: Example input with missing values Uncertainty is prevalent in data analysis, no matter what the size of the data, the application domain, or the type of analysis. Common sources of uncertainty include missing values, sensor errors and noise, bias, outliers, mismatched data, and many more. If ignored, data uncertainty can result in hard to trace errors in analytical results, which in turn can have severe real world implications like unfounded scientific discoveries, financial damages, or even effects on people’s physical well-being (e.g., medical decisions based on incorrect data).