Visual analytics

C. Bors et al., “A Provenance Task Abstraction Framework”, IEEE Computer Graphics and Applications, vol. 39, no. 6, p. 15, 2019.
V. Filipov, Arleo, A., Federico, P., and Miksch, S., “CV3: Visual Exploration, Assessment, and Comparison of CVs”, Computer Graphics Forum, vol. 38, no. 3, p. 11, 2019.
A. Walch, Schwärzler, M., Luksch, C., Eisemann, E., and Gschwandtner, T., “LightGuider: Guiding Interactive Lighting Design using Suggestions, Provenance, and Quality Visualization”, IEEE Transactions on Visualization and Computer Graphics (TVCG), vol. 26, no. 1, p. 10, 2020.
N. Andrienko et al., “Viewing Visual Analytics as Model Building”, Computer Graphics Forum, vol. 37, no. 1, pp. 275–299, 2018.
R. A. Leite, Gschwandtner, T., Miksch, S., Gstrein, E., and Kuntner, J., “Network Analysis for Financial Fraud Detection”, in EuroVis 2018 - Posters, 2018.
D. Ceneda, Gschwandtner, T., May, T., Miksch, S., Streit, M., and Tominski, C., “Guidance or No Guidance? A Decision Tree Can Help”, in EuroVA: International Workshop on Visual Analytics, 2018, pp. 19–23.
C. Bors, Gschwandtner, T., and Miksch, S., “Visually Exploring Data Provenance and Quality of Open Data”, in EuroVis 2018 - Posters, 2018, pp. 9–11.
J. Bernard et al., “Combining the Automated Segmentation and Visual Analysis of Multivariate Time Series”, in EuroVis Workshop on Visual Analytics (EuroVA) 2018, 2018, pp. 49–53.
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.
D. Ceneda et al., “Characterizing Guidance in Visual Analytics”, IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, pp. 111-120, 2017.