Communicating Data Quality in On-Demand Curation
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On-demand curation (ODC) tools like Paygo, KATARA, and Mimir allow users to defer expensive curation effort until it is necessary. In contrast to classical databases that do not even permit potentially erroneous data to be queried, ODC systems instead answer with guesses or approximations. The quality and scope of these guesses may vary and it is critical that an ODC system be able to communicate this information to an end-user. The central contribution is a preliminary user study evaluating the cognitive burden and expressiveness of four representations of "attribute-level'' uncertainty. The study shows (1) insignificant differences in time taken for users to interpret the four types of uncertainty tested, and (2) that different presentations of uncertainty change the way people interpret and react to data. A follow up study was conducted, and ultimately, we show that a set of UI design guidelines and best practices for conveying uncertainty will be necessary for ODC tools to be effective. This thesis consists of the first step towards establishing such guidelines.