Communicating Data Quality in On-Demand Curation

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.

Grantor dc:publisher

State University of New York at Buffalo

Year dc:date.issued

2018

Author dc:creator

• Kumari, Poonam; 0000-0002-3940-6986

Contributors dc:contributor

• Kennedy, Oliver
• Computer Science and Engineering

• computer science

Statement dc:rights

• Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.
• Copyright retained by author.

Language dc:language

eng

Handle dc:identifier

http://hdl.handle.net/10477/77954

OAI record oai:identifier

https://ubir.buffalo.edu/oai/request?verb=GetRecord&metadataPrefix=qdc&identifier=oai%3Aubir.buffalo.edu%3A10477%2F77954