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Contains the keyword data quality

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Gschwandtner T, Erhart O. Know Your Enemy: Identifying Quality Problems of Time Series Data. In: IEEE Pacific Visualization Symposium (PacificVis '18). Kobe, Japan: IEEE Xplore Digital Library; 2018. p. 205-14.application/pdf iconpaper
2018
Rind A, Federico P, Gschwandtner T, Aigner W, Doppler J, Wagner M. Visual Analytics of Electronic Health Records with a Focus on Time. In: Rinaldi G, editor. New Perspectives in Medical Records: Meeting the Needs of Patients and Practitioners. Springer; 2017. p. 65-77. (TELe-Health).
2017
Bors C, Gschwandtner T, Miksch S. QualityFlow: Provenance Generation from Data Quality. In: Maciejewski R, Marton F, editors. Proceedings of the Eurographics Conference on Visualization (EuroVis) - Posters 2015. Cagliari, Sardinia, Italy: The Eurographics Association; 2015. 3.application/pdf iconposter application/pdf iconpaper
2015
Bors C, Gschwandtner T, Miksch S, Gärtner J. QualityTrails: Data Quality Provenance as a Basis for Sensemaking. In: Xu K, Attfield S, Jankun-Kelly TJ, editors. Proceedings of the IEEE VIS Workshop on Provenance for Sensemaking.; 2014. p. 1-2.application/pdf iconworkshop_provenance_poster_final.pdf application/pdf iconpaper
2014
Gschwandtner T, Aigner W, Miksch S, Gärtner J, Kriglstein S, Pohl M, et al. TimeCleanser: A Visual Analytics Approach for Data Cleansing of Time-Oriented Data. In: Lindstaedt S, Granitzer M, Sack H, editors. 14th International Conference on Knowledge Technologies and Data-driven Business (i-KNOW 2014). Graz, Austria: ACM Press; 2014. p. 1-8.application/pdf iconpaper.pdf
2014
Gschwandtner T, Gärtner J, Aigner W, Miksch S. A Taxonomy of Dirty Time-Oriented Data. In: Quirchmayr G, Basl J, You I, Xu L, Weippl E, editors. Lecture Notes in Computer Science (LNCS 7465): Multidisciplinary Research and Practice for Information Systems (Proceedings of the CD-ARES 2012). Prague, Czech Republic: Springer, Berlin / Heidelberg; 2012. p. 58-72.application/pdf iconpaper
2012