在按需管理中传达数据质量
Communicating Data Quality in On-Demand Curation
摘要：
按需策展（ODC）工具，如Paygo，KATARA和Mimir允许用户推迟昂贵的策展工作，直到必要时。与不响应对潜在错误数据的查询的经典数据库相反，ODC系统用猜测或近似来回答。这些猜测的质量和范围可能会有所不同，ODC系统能够将此信息传达给最终用户至关重要。本文的核心贡献是一个初步的用户研究评估的认知负担和表现力的四个表征的“属性级”的不确定性。该研究表明：（1）用户解释测试的四种不确定性所需的时间无显著差异;（2）不确定性的不同表现会改变人们解释和反应数据的方式。最终，我们表明，一套UI设计指南和最佳实践传达不确定性将是必要的ODC工具是有效的。这份文件是制定这种准则的第一步。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
