Communicating Data Quality in On-Demand Curation
Communicating Data Quality in On-Demand Curation
复制标题
在按需管理中传达数据质量
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Oliver Kennedy
中科院分区：
文献类型：
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作者：
P. Kumari;Said Achmiz;Oliver Kennedy
Paygo、Katara和Mimir等按需管理(ODC)工具允许用户将昂贵的管理工作推迟到必要时进行。与不响应对潜在错误数据的查询的传统数据库不同，ODC系统相反地用猜测或近似来回答。这些猜测的质量和范围可能有所不同，ODC系统能够将这些信息传达给最终用户是至关重要的。这篇论文的中心贡献是一项初步的用户研究，评估了四种属性级别不确定性的表现形式的认知负担和表达能力。这项研究表明，(1)用户解释四种测试的不确定性所需的时间没有显著差异，(2)不同的不确定性表述会改变人们对数据的解释和反应方式。最后，我们展示了一套传达不确定性的用户界面设计指南和最佳实践对于ODC工具的有效性是必要的。这份文件是建立这种指导方针的第一步。
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.