Visual analytics

B. Vago, Archambault, D., and Arleo, A., “DynTrix: A Hybrid Representation for Dynamic Graphs”, Computer Graphics Forum, vol. 43, no. 3, 2024.
C. Di Ciccio, Miksch, S., Soffer, P., Weber, B., and Meroni, G., “Human in the (Process) Mines (Dagstuhl Seminar 23271)”, Dagstuhl Reports, vol. 13, pp. 1-33, 2024.
T. Kamencek, Filipov, V., Schetinger, V., Miksch, S., and Rosenberg, R., “TimeScapes: Towards a Visual Characterization of Modern Artist Exhibition Acitvity”. 2023.
V. Filipov, Arleo, A., von Landesberger, T., and Archambault, D., “Back to the Graphs: A Collection of Datasets and Quality Criteria for Temporal Networks Layout and Visualization”. 2023.
D. Ceneda, Collins, C., El-Assady, M., Miksch, S., Tominski, C., and Arleo, A., “A heuristic approach for dual expert/end-user evaluation of guidance in visual analytics”, IEEE Transactions on Visualization and Computer Graphics, vol. 30, pp. 997-1007, 2024.
J. Schmidt, Pointner, B., and Miksch, S., “Visual Analytics for Understanding Draco s Knowledge Base”, IEEE Transactions on Visualization and Computer Graphics, vol. 30, pp. 392-402, 2024.
S. Miksch, “Visual Analytics Meets Temporal Reasoning: Challenges and Opportunities”, vol. 247. Schloss Dagstuhl — Leibniz-Zentrum für Informatik, Dagstuhl, Germany, pp. 1-2, 2022.
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, vol. 28, no. 12, p. 12, 2022.
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.