data quality

C. Bors, “Facilitating Data Quality Assessment Utilizing Visual Analytics: Tackling Time, Metrics, Uncertainty, and Provenance”, TU Wien, Vienna, 2020.

C. Bors, Gschwandtner, T., and Miksch, S., “Capturing and Visualizing Provenance From Data Wrangling”, IEEE Computer Graphics and Applications, vol. 39, no. 6, p. 15, 2019.

T. Gschwandtner and Erhart, O., “Know Your Enemy: Identifying Quality Problems of Time Series Data”, in IEEE Pacific Visualization Symposium (PacificVis '18), 2018, pp. 205-214.

A. Rind, Federico, P., Gschwandtner, T., Aigner, W., Doppler, J., and Wagner, M., “Visual Analytics of Electronic Health Records with a Focus on Time”, in New Perspectives in Medical Records: Meeting the Needs of Patients and Practitioners, Springer, 2017, pp. 65-77.

C. Bors, Gschwandtner, T., and Miksch, S., “QualityFlow: Provenance Generation from Data Quality”, in Proceedings of the Eurographics Conference on Visualization (EuroVis) - Posters 2015, 2015, p. 3.

C. Bors, Gschwandtner, T., Miksch, S., and Gärtner, J., “QualityTrails: Data Quality Provenance as a Basis for Sensemaking”, in Proceedings of the IEEE VIS Workshop on Provenance for Sensemaking, 2014, pp. 1–2.

T. Gschwandtner et al., “TimeCleanser: A Visual Analytics Approach for Data Cleansing of Time-Oriented Data”, in 14th International Conference on Knowledge Technologies and Data-driven Business (i-KNOW 2014), 2014, pp. 1-8.

T. Gschwandtner, Gärtner, J., Aigner, W., and Miksch, S., “A Taxonomy of Dirty Time-Oriented Data”, in Lecture Notes in Computer Science (LNCS 7465): Multidisciplinary Research and Practice for Information Systems (Proceedings of the CD-ARES 2012), 2012, p. 58 -- 72.