  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 respond to queries over potentially erroneous data, 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 of this
paper 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. 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 paper
represents the first step towards establishing such guidelines.
