Visual analytics

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.

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.

M. Röhlig et al., “Supporting Activity Recognition by Visual Analytics”, in Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 2015.

M. Bögl et al., “Visually and Statistically Guided Imputation of Missing Values in Univariate Seasonal Time Series”, in Poster Proceedings of the IEEE Visualization Conference 2015, 2015.

M. Wagner et al., “A Survey of Visualization Systems for Malware Analysis”, in Eurographics Conference on Visualization (EuroVis) State of The Art Reports, 2015, pp. 105–125.

T. Gschwandtner et al., “Enhancing Time Series Segmentation and Labeling Through the Knowledge Generation Model”, in Poster Proceedings of the Eurographics Conference on Visualization (EuroVis 2015), 2015, p. 3.