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Bors, C., Wenskovitch, J., Dowling, M., Attfield, S., Battle, L., Endert, A., Kulyk, O., & Laramee, R. S. (2019). A Provenance Task Abstraction Framework. IEEE Computer Graphics and Applications, 39(6), 46–60. https://doi.org/10.1109/mcg.2019.2945720 ( reposiTUm)

Bögl, M., Filzmoser, P., Gschwandtner, T., Lammarsch, T., Leite, R. A., Miksch, S., & Rind, A. (2017). Cycle Plot Revisited: Multivariate Outlier Detection Using a Distance-Based Abstraction. Computer Graphics Forum, 36(3), 227–238. http://hdl.handle.net/20.500.12708/146628 ( reposiTUm)

Bors, C., Bernard, J., Bögl, M., Gschwandtner, T., Kohlhammer, J., & Miksch, S. (2019). Quantifying Uncertainty in Multivariate Time Series Pre-Processing. In T. von Landesberger & C. Turkay (Eds.), EuroVis Workshop on Visual Analytics (pp. 31–35). Proceedings of the 21st EG/VGTC Conference on Visualization (EuroVis 2019). https://doi.org/10.2312/eurova.20191121 ( reposiTUm)

Bernard, J., Hutter, M., Reinemuth, H., Pfeifer, H., Bors, C., & Kohlhammer, J. (2019). Visual-Interactive Preprocessing of Multivariate Time Series Data. In A. Ferreira & J. Jorge (Eds.), Eurographics / IEEE VGTC Conference on Visualization 2019 (pp. 401–412). Proceedings of the 21st EG/VGTC Conference on Visualization (EuroVis 2019). http://hdl.handle.net/20.500.12708/57810 ( reposiTUm)

Bernard, J., Bors, C., Bögl, M., Eichner, C., Gschwandtner, T., Miksch, S., Schumann, H., & Kohlhammer, J. (2018). Combining the Automated Segmentation and Visual Analysis of Multivariate Time Series. In C. Tomonski & T. von Landesberger (Eds.), EuroVis Workshop on Visual Analytics (EuroVA) 2018 (pp. 49–53). Eurographics Digital Library. https://doi.org/10.2312/eurova.20181112 ( reposiTUm)

Bögl, M., Bors, C., Gschwandtner, T., & Miksch, S. (2018). Categorizing Uncertainties in the Process of Segmenting and Labeling Time Series Data. In A. Puig & R. Raidou (Eds.), EuroVis 2018 - Posters (pp. 45–47). The Eurographics Association. https://doi.org/10.2312/eurp.20181126 ( reposiTUm)

Bors, C., Bögl, M., Gschwandtner, T., & Miksch, S. (2017). Visual support for rastering of unequally spaced time series. In R. P. Biuk-Aghai, J. Li, & S. Takahashi (Eds.), Proceedings of the 10th International Symposium on Visual Information Communication and Interaction. ACM International Conference Proceeding Series. https://doi.org/10.1145/3105971.3105984 ( reposiTUm)

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Bögl, M., Bors, C., Gschwandtner, T., & Miksch, S. (2018). Uncertainty types in segmenting and labeling time series data. Data Science, Statistics & Visualisation, Lissabon, Portugal. http://hdl.handle.net/20.500.12708/86861 ( reposiTUm)

Bors, C., Bögl, M., Bernard, J., Gschwandtner, T., & Miksch, S. (2018). Quantifying Uncertainty in Time Series Data Processing. VisInPractice Mini-Symposium on Visualizing Uncertainty, Berlin, Germany. http://hdl.handle.net/20.500.12708/86740 ( reposiTUm)

Bögl, M., Filzmoser, P., Gschwandtner, T., Lammarsch, T., Leite, R. A., Miksch, S., & Rind, A. (2017). Cycle Plot Revisited: Multivariate Outlier Detection Using a Distance-Based Abstraction. Eurographics / IEEE VGTC Conference on Visualization (EuroVis 2017), Barcelona, Spain. http://hdl.handle.net/20.500.12708/86509 ( reposiTUm)

Bors, C., Bögl, M., Gschwandtner, T., & Miksch, S. (2017). Visual Support for Rastering of Unequally Spaced Time Series. Data Science, Statistics & Visualisation, Lissabon, Portugal. http://hdl.handle.net/20.500.12708/86514 ( reposiTUm)