Bors, C., Gschwandtner, T., Miksch, S., & Gärtner, J. (2014). QualityTrails: Data Quality Provenance as a Basis for Sensemaking. In K. Xu, S. Attfield, & T. J. Jankun-Kelly (Eds.), Proceedings of the IEEE VIS Workshop on Provenance for Sensemaking (pp. 1–2). http://hdl.handle.net/20.500.12708/55302
Proceedings of the IEEE VIS Workshop on Provenance for Sensemaking
IEEE VIS Workshop on Provenance for Sensemaking
Number of Pages:
data quality; data provenance; analytic provenance; sensemaking; quality metrics; visual data analysis
Visual Analytics prototypes increasingly support human sensemaking through providing Provenance information. For data analysts the challenge of knowledge generation starts with assessing the quality of a data set, but Provenance is not yet utilized to aid this task. This position paper aims at characterizing the complexity of Visual Analytics methods introducing Provenance in Data Quality by highlighting the challenges of (1) generating Provenance from Data Quality Control and (2) sensemaking based on Data Quality Provenance.
CVAST: Centre for Visual Analytics Science and Technology (Laura Bassi Centre of Expertise)
Business Informatics: 20% Visual Computing and Human-Centered Technology: 80%