data quality

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