TU Wien Informatics

20 Years
Overview Professors Scientific Staff Administrative Staff Student Staff External Lecturers By Responsibilities By Committee By Name
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Markus Bögl

Univ.Ass. Dipl.-Ing. Dr.techn. / BSc

Markus Bögl

Role

Contact

2024W

2025S

 

2024

2023

  • Data Type Agnostic Visual Sensitivity Analysis / Piccolotto, N., Bogl, M., Muehlmann, C., Nordhausen, K., Filzmoser, P., Schmidt, J., & Miksch, S. (2023). Data Type Agnostic Visual Sensitivity Analysis. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/TVCG.2023.3327203
  • Data Type Agnostic Visual Sensitivity Analysis / Piccolotto, N., Bögl, M., Mühlmann, C., Nordhausen, K., Filzmoser, P., Schmidt, J., & Miksch, S. (2023, October 26). Data Type Agnostic Visual Sensitivity Analysis [Conference Presentation]. IEEE VIS 2023, Melbourne, Australia. http://hdl.handle.net/20.500.12708/189927
    Project: GuidedVA (2020–2025)
  • Visual Parameter Space Exploration in Time and Space / Piccolotto, N., Bögl, M., & Miksch, S. (2023, June 14). Visual Parameter Space Exploration in Time and Space [Conference Presentation]. 25th EG Conference on Visualization (EuroVis 2023), Leipzig, Germany. http://hdl.handle.net/20.500.12708/187025
  • Multi-Ensemble Visual Analytics via Fuzzy Sets / Piccolotto, N., Bögl, M., & Miksch, S. (2023, June 12). Multi-Ensemble Visual Analytics via Fuzzy Sets [Conference Presentation]. EuroVis Workshop on Visual Analytics (EuroVA 2023), Leipzig, Germany. http://hdl.handle.net/20.500.12708/187032
  • Visual Parameter Space Exploration in Time and Space / Piccolotto, N., Bögl, M., & Miksch, S. (2023). Visual Parameter Space Exploration in Time and Space. Computer Graphics Forum, 2023. https://doi.org/10.1111/cgf.14785
  • On Time and Space: An Experimental Study on Graph Structural and Temporal Encodings / Filipov, V., Arleo, A., Bögl, M., & Miksch, S. (2023). On Time and Space: An Experimental Study on Graph Structural and Temporal Encodings. In P. Angelini & R. von Hanxleden (Eds.), Graph Drawing and Network Visualization. GD 2022 (pp. 271–288). Springer Cham. https://doi.org/10.1007/978-3-031-22203-0_20
    Project: ArtVis (2022–2025)
  • Multi-Ensemble Visual Analytics via Fuzzy Sets / Piccolotto, N., Bögl, M., & Miksch, S. (2023). Multi-Ensemble Visual Analytics via Fuzzy Sets. In M. Angelini & M. El-Assady (Eds.), EuroVis Workshop on Visual Analytics (EuroVA) (pp. 25–30). The Eurographics Association. https://doi.org/10.2312/eurova.20231092
  • On network structural and temporal encodings: a space and time odyssey / Filipov, V., Arleo, A., Bögl, M., & Miksch, S. (2023). On network structural and temporal encodings: a space and time odyssey. IEEE Transactions on Visualization and Computer Graphics, 14(8). https://doi.org/10.34726/5410
    Download: PDF (1.25 MB)
    Project: ArtVis (2022–2025)

2022

2020

2019

  • Quantifying Uncertainty in Multivariate Time Series Pre-Processing / 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
    Project: VISSECT (2016–2020)

2018

2017

  • Cycle Plot Revisited: Multivariate Outlier Detection Using a Distance-Based Abstraction / 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, EU. http://hdl.handle.net/20.500.12708/86509
    Project: VISSECT (2016–2020)
  • Cycle Plot Revisited: Multivariate Outlier Detection Using a Distance-Based Abstraction / 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
    Project: VISSECT (2016–2020)
  • Visual Support for Rastering of Unequally Spaced Time Series / Bors, C., Bögl, M., Gschwandtner, T., & Miksch, S. (2017). Visual Support for Rastering of Unequally Spaced Time Series. Data Science, Statistics & Visualisation, Lissabon, EU. http://hdl.handle.net/20.500.12708/86514
    Project: VISSECT (2016–2020)
  • Visual support for rastering of unequally spaced time series / 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
    Project: VISSECT (2016–2020)

2016

  • Visual-Interactive Segmentation of Multivariate Time Series / Bernard, J., Dobermann, E., Bögl, M., Röhlig, M., Vögele, A., & Kohlhammer, J. (2016). Visual-Interactive Segmentation of Multivariate Time Series. In N. Andrienko & M. Sedlmair (Eds.), EuroVA 2016 EuroVis Workshop on Visual Analytics (pp. 31–35). The Eurographics Association. https://doi.org/10.2312/eurova.20161121
    Project: VISSECT (2016–2020)
  • Visual Encodings of Temporal Uncertainty: A Comparative User Study / Gschwandtner, T., Bögl, M., Federico, P., & Miksch, S. (2016). Visual Encodings of Temporal Uncertainty: A Comparative User Study. IEEE Transactions on Visualization and Computer Graphics, 22(1), 539–548. https://doi.org/10.1109/tvcg.2015.2467752
  • Guiding the Visualization of Time-oriented Data / Ceneda, D., Aigner, W., Bögl, M., Gschwandtner, T., & Miksch, S. (2016). Guiding the Visualization of Time-oriented Data. In Proceedings of IEEE VIS. IEEE Visualization, Minneapolis, USA, Austria. http://hdl.handle.net/20.500.12708/56578

2015

  • Supporting Activity Recognition by Visual Analytics / Röhlig, M., Luboschik, M., Bögl, M., Krüger, F., Alsallakh, B., Miksch, S., Kirste, T., & Schumann, H. (2015). Supporting Activity Recognition by Visual Analytics. In Proceedings of the IEEE Conference on Visual Analytics Science and Technology (p. 8). IEEE. http://hdl.handle.net/20.500.12708/56131
  • Enhancing Time Series Segmentation and Labeling Through the Knowledge Generation Model / Gschwandtner, T., Schumann, H., Bernard, J., May, T., Bögl, M., Miksch, S., Kohlhammer, J., Röhlig, M., & Alsallakh, B. (2015). Enhancing Time Series Segmentation and Labeling Through the Knowledge Generation Model. In R. Maciejewski & F. Marton (Eds.), Proceedings of the Eurographics Conference on Visualization (EuroVis) - Posters 2015 (p. 3). Eurographics Association. http://hdl.handle.net/20.500.12708/56049
  • Integrating Predictions in Time Series Model Selection / Bögl, M., Aigner, W., Filzmoser, P., Gschwandtner, T., Lammarsch, T., Miksch, S., & Rind, A. (2015). Integrating Predictions in Time Series Model Selection. In J. Yang, E. Bertini, N. Elmqvist, T. Dwyer, X. Yuan, & H. Carr (Eds.), EuroVA 2015 EuroVis Workshop on Visual Analytics (pp. 73–78). The Eurographics Association. https://doi.org/10.2312/eurova.20151107
    Project: HypoVis (2011–2015)
  • Visually and Statistically Guided Imputation of Missing Values in Univariate Seasonal Time Series / Bögl, M., Aigner, W., Filzmoser, P., Gschwandtner, T., Lammarsch, T., Miksch, S., & Rind, A. (2015). Visually and Statistically Guided Imputation of Missing Values in Univariate Seasonal Time Series. In J. Yang, E. Bertini, N. Elmqvist, T. Dwyer, X. Yuan, & H. Carr (Eds.), Poster Proceedings of the IEEE Visualization Conference 2015 (p. 2). http://hdl.handle.net/20.500.12708/56130
  • Visual Exploration and Analysis of Uncertain Time-oriented Data / Bögl, M. (2015). Visual Exploration and Analysis of Uncertain Time-oriented Data. In E. Marai, C. Collins, & M. Pohl (Eds.), Proceedings of the IEEE VIS 2015 Doctoral Colloquium - closed, invitation only special session (p. 4). http://hdl.handle.net/20.500.12708/56037

2014

  • Analyzing Parameter Influence on Time-Series Segmentation and Labeling / Röhlig, M., Luboschik, M., Schumann, H., Bögl, M., Alsallakh, B., & Miksch, S. (2014). Analyzing Parameter Influence on Time-Series Segmentation and Labeling. In G. Andrienko, E. Bertini, H. Carr, N. Elmqvist, B. Lee, & H. Leitte (Eds.), Poster Proceedings of the IEEE Visualization Conference 2014. http://hdl.handle.net/20.500.12708/55193
  • A Visual Analytics Approach to Segmenting and Labeling Multivariate Time Series Data / Alsallakh, B., Bögl, M., Gschwandtner, T., Miksch, S., Esmael, B., Arnaout, A., Thonhauser, G., & Zöllner, P. (2014). A Visual Analytics Approach to Segmenting and Labeling Multivariate Time Series Data. In M. Pohl & J. C. Roberts (Eds.), EuroVis Workshop on Visual Analytics (EuroVA) (pp. 31–35). Eurographics. https://doi.org/10.2312/eurova.20141142
  • Visual Analytics Methods to Guide Diagnostics for Time Series Model Predictions / Bögl, M., Aigner, W., Filzmoser, P., Gschwandtner, T., Lammarsch, T., Miksch, S., & Rind, A. (2014). Visual Analytics Methods to Guide Diagnostics for Time Series Model Predictions. In Proceedings of the 2014 IEEE VIS Workshop on Visualization for Predictive Analytics (p. 4). http://hdl.handle.net/20.500.12708/55730

2013

  • Visual Analytics for Model Selection in Time Series Analysis / Bögl, M., Aigner, W., Filzmoser, P., Lammarsch, T., Miksch, S., & Rind, A. (2013). Visual Analytics for Model Selection in Time Series Analysis. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2237–2246. https://doi.org/10.1109/tvcg.2013.222
    Project: HypoVis (2011–2015)
  • Visual Analytics for Model Selection in Time Series Analysis / Bögl, M., Aigner, W., Filzmoser, P., Lammarsch, T., Miksch, S., & Rind, A. (2013). Visual Analytics for Model Selection in Time Series Analysis. IEEE Conference on Visual Analytics Science and Technology (IEEE VAST), Atlanta, GA, USA, Non-EU. http://hdl.handle.net/20.500.12708/85611
    Project: HypoVis (2011–2015)
  • Interactive Visual Transformation for Symbolic Representation of Time-Oriented Data / Lammarsch, T., Aigner, W., Bertone, A., Bögl, M., Gschwandtner, T., Miksch, S., & Rind, A. (2013). Interactive Visual Transformation for Symbolic Representation of Time-Oriented Data. In A. Holzinger, M. Ziefle, & V. Glavinić (Eds.), Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data (pp. 400–419). Springer. https://doi.org/10.1007/978-3-642-39146-0_37