Visual analytics

D. Ceneda, Arleo, A., Gschwandtner, T., and Miksch, S., “Show Me Your Face: Towards an Automated Method to Provide Timely Guidance in Visual Analytics”, IEEE Transactions on Visualization and Computer Graphics, p. 12, 2021.
M. Bögl, “Visual Analysis of Periodic Time Series Data - Supporting Model Selection, Prediction, Imputation, and Outlier Detection Using Visual Analytics”, TU Wien, 2020.
C. Bors, Eichner, C., Miksch, S., Tominski, C., Schumann, H., and Gschwandtner, T., “Exploring Time Series Segmentations Using Uncertainty and Focus+Context Techniques”, in EuroVis 2020, 2020.
N. Andrienko, Andrienko, G., Miksch, S., Schumann, H., and Wrobel, S., “A Theoretical Model for Pattern Discovery in Visual Analytics”, Visual Informatics, vol. 5, no. 1, p. 20, 2021.
D. Ceneda, “Guidance-Enriched Visual Analytics”, Vienna University of Technology, Vienna, 2020.
R. A. Leite, Gschwandtner, T., Miksch, S., Gstrein, E., and Kuntner, J., “NEVA: Visual Analytics to Identify Fraudulent Networks”, Computer Graphics Forum, vol. 39, no. 6, 2020.
D. Ceneda et al., “Guide Me in Analysis: A Framework for Guidance Designers”, Computer Graphics Forum, vol. 39, no. 6, p. 19, 2020.
C. Bors, “Facilitating Data Quality Assessment Utilizing Visual Analytics: Tackling Time, Metrics, Uncertainty, and Provenance”, TU Wien, Vienna, 2020.
V. Schetinger, Raminger, K., Filipov, V., Soursos, N., Zapke, S., and Miksch, S., “Bridging the Gap between Visual Analytics and Digital Humanities: Beyond the Data-Users-Tasks Design Triangle”. 2020.
C. Bors et al., “A Provenance Task Abstraction Framework”, IEEE Computer Graphics and Applications, vol. 39, no. 6, p. 15, 2019.