2022W

• Interdisciplinary Project in Data Science / 194.047 / PR

2023S

• Interdisciplinary Project in Data Science / 194.060 / PR

• Feature detection in multidimensional datasets of lubricated contacts
  2021 – 2024 / Austrian Research Promotion Agency (FFG)
• Synthesis of Disease Spread and Network Reduction Data for COVID-19 Simulation
  2020 – 2021 / Vienna Science and Technology Fund (WWTF)
• Decision Support for Health Policy and Planning: Methods, Models and Technologies based on existing health care data
  2014 – 2019 / Austrian Research Promotion Agency (FFG)

Note: Due to the rollout of TU Wien’s new publication database, the list below may be slightly outdated. Once the migration is complete, everything will be up to date again.

2023

• Massive Data Sets – Is Data Quality Still an Issue? / Filzmoser, P., & Mazak-Huemer, A. (2023). Massive Data Sets – Is Data Quality Still an Issue? In B. Vogel-Heuser & M. Wimmer (Eds.), Digital Transformation (Vol. 1, pp. 269–279). Springer Vieweg. https://doi.org/10.1007/978-3-662-65004-2_11

2022

• TBSSvis: Visual analytics for temporal blind source separation / Piccolotto, N., Bögl, M., Gschwandtner, T., Muehlmann, C., Nordhausen, K., Filzmoser, P., & Miksch, S. (2022). TBSSvis: Visual analytics for temporal blind source separation. Visual Informatics, 6(4), 51–66. https://doi.org/10.1016/j.visinf.2022.10.002
• Visual Parameter Selection for Spatial Blind Source Separation / Piccolotto, N., Bögl, M., Mühlmann, C., Nordhausen, K., Filzmoser, P., & Miksch, S. (2022, June 15). Visual Parameter Selection for Spatial Blind Source Separation [Conference Presentation]. EuroVis 2022, Rome, Italy.
• Visual Parameter Selection for Spatial Blind Source Separation / Piccolotto, N., Bögl, M., Muehlmann, C., Nordhausen, K., Filzmoser, P., & Miksch, S. (2022). Visual Parameter Selection for Spatial Blind Source Separation. Computer Graphics Forum, 41(3), 157–168. https://doi.org/10.1111/cgf.14530

2021

• Local projections for high-dimensional outlier detection / Ortner, T., Filzmoser, P., Rohm, M., Brodinova, S., & Breiteneder, C. (2021). Local projections for high-dimensional outlier detection. METRON, 79(2), 189–206. https://doi.org/10.1007/s40300-020-00183-5

2020

• The impact of COVID-19 on relative changes in aggregated mobility using mobile-phone data / Heiler, G., Hanbury, A., & Filzmoser, P. (2020). The impact of COVID-19 on relative changes in aggregated mobility using mobile-phone data (p. 14). arXiv. https://doi.org/10.48550/arXiv.2009.03798
• Robust and sparse k-means clustering in high dimension / Filzmoser, P., Brodinova, S., Ortner, T., Breiteneder, C., & Rohm, M. (2020). Robust and sparse k-means clustering in high dimension. Seminarvortrag an der JKU Linz, Linz, Austria. http://hdl.handle.net/20.500.12708/123091

2019

• A Comprehensive Prediction Approach for Hardware Asset Management / Wurl, A., Falkner, A., Filzmoser, P., Haselböck, A., Mazak, A., & Sperl, S. (2019). A Comprehensive Prediction Approach for Hardware Asset Management. In C. Quix & J. Bernardino (Eds.), Communications in Computer and Information Science (pp. 26–49). Springer Nature Schwitzerland AG 2019. https://doi.org/10.1007/978-3-030-26636-3_2
• Exploring robustness in a combined feature selection approach. / Wurl, A., Falkner, A., Haselböck, A., Mazak, A., & Filzmoser, P. (2019). Exploring robustness in a combined feature selection approach. In Proceedings of the 8th International Conference on Data Science, Technology and Applications. Scitepress - Science and Technology Publications, LDA. https://doi.org/10.5220/0007924400840091
• Robust k-means-based clustering for high-dimensional data / Filzmoser, P., Brodinova, S., Ortner, T., Breiteneder, C., & Rohm, M. (2019). Robust k-means-based clustering for high-dimensional data. International Conference on Robust Statistics (ICORS 2019), Guayaquil, Non-EU. http://hdl.handle.net/20.500.12708/122853

2018

• Dynamic log file analysis: An unsupervised cluster evolution approach for anomaly detection / Landauer, M., Wurzenberger, M., Skopik, F., Settanni, G., & Filzmoser, P. (2018). Dynamic log file analysis: An unsupervised cluster evolution approach for anomaly detection. Computers and Security, 79, 94–116. https://doi.org/10.1016/j.cose.2018.08.009
• Guided projections for analyzing the structure of high-dimensional data / Ortner, T., Filzmoser, P., Rohm, M., Breiteneder, C., & Brodinova, S. (2018). Guided projections for analyzing the structure of high-dimensional data. Journal of Computational and Graphical Statistics, 27(4), 750–762. https://doi.org/10.1080/10618600.2018.1459304
• Time series analysis: Unsupervised anomaly detection beyond outlier detection / Landauer, M., Wurzenberger, M., Skopik, F., Settanni, G., & Filzmoser, P. (2018). Time series analysis: Unsupervised anomaly detection beyond outlier detection. In C. Su & H. Kikuch (Eds.), Information Security Practice and Experience (pp. 16–36). Springer. http://hdl.handle.net/20.500.12708/29783

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. Computer Graphics Forum, 36(3), 227–238. http://hdl.handle.net/20.500.12708/146628 / Project: VISSECT
• The paradigm of relatedness / Grad-Gyenge, L., & Filzmoser, P. (2017). The paradigm of relatedness. In W. Abramowicz, R. Alt, & B. Franczyk (Eds.), Business Information Systems Workshops  BIS 2016 International Workshops, Leipzig, Germany, July 6-8, 2016, Revised Papers (pp. 57–68). Springer. https://doi.org/10.1007/978-3-319-52464-1_6
• Grouping and outlier detection using robust sparse clustering / Brodinova, S., Filzmoser, P., Ortner, T., Zaharieva, M., & Breiteneder, C. (2017). Grouping and outlier detection using robust sparse clustering. Olomouc Days of Applied Mathematics (ODAM 2017), Olomouc, EU. http://hdl.handle.net/20.500.12708/122037 / Project: FAMOUS
• Finding groups in large and high-dimensional data using a k-means-based algorithm / Brodinova, S., Filzmoser, P., Ortner, T., Breiteneder, C., & Zaharieva, M. (2017). Finding groups in large and high-dimensional data using a k-means-based algorithm. MOVISS - Metabolomic Bio & Data 2017, Vorau, Austria. http://hdl.handle.net/20.500.12708/122033 / Project: FAMOUS
• Local projection for outlier detection / Ortner, T., Filzmoser, P., Brodinova, S., Zaharieva, M., & Breiteneder, C. (2017). Local projection for outlier detection. Olomouc Days of Applied Mathematics (ODAM 2017), Olomouc, EU. http://hdl.handle.net/20.500.12708/122019 / Project: FAMOUS
• 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
• Robust and sparse clustering for high-dimensional data / Brodinova, S., Filzmoser, P., Ortner, T., Zaharieva, M., & Breiteneder, C. (2017). Robust and sparse clustering for high-dimensional data. In CLADAG 2017 Book of Short Papers. Conference of the CLAssification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS), Milan, Italy, EU. http://hdl.handle.net/20.500.12708/57014 / Project: FAMOUS

2016

• Guided projections for analysising the structure of high dimensional data / Ortner, T., Filzmoser, P., Zaharieva, M., Breiteneder, C., & Brodinova, S. (2016). Guided projections for analysising the structure of high dimensional data. International Conference of the ERCIM WG on Computational and Methodological Statistics, Seville, Spain, EU. http://hdl.handle.net/20.500.12708/121546
• Forward Projection for High-Dimensional Data / Ortner, T., Filzmoser, P., Brodinova, S., Zaharieva, M., & Breiteneder, C. (2016). Forward Projection for High-Dimensional Data. International Conference COMPUTER DATA ANALYSIS & MODELING, Minsk, Belarus, Non-EU. http://hdl.handle.net/20.500.12708/86324 / Project: FAMOUS
• Group Detection in the Context of Imbalanced Data / Brodinova, S., Zaharieva, M., Filzmoser, P., Ortner, T., & Breiteneder, C. (2016). Group Detection in the Context of Imbalanced Data. International Conference COMPUTER DATA ANALYSIS & MODELING, Minsk, Belarus, Non-EU. http://hdl.handle.net/20.500.12708/86323 / Project: FAMOUS
• Evaluation of robust PCA for supervised audio outlier detection / Brodinova, S., Ortner, T., Filzmoser, P., Zaharieva, M., & Breiteneder, C. (2016). Evaluation of robust PCA for supervised audio outlier detection. In Proceeding of 22nd International Conference on Computational Statistics (COMPSTAT) (p. 12). http://hdl.handle.net/20.500.12708/56525 / Project: FAMOUS
• Recommendation Techniques on a Knowledge Graph for Email Remarketing / Grad-Gyenge, L., & Filzmoser, P. (2016). Recommendation Techniques on a Knowledge Graph for Email Remarketing. In eKNOW 2016, The Eighth International Conference on Information, Process, and Knowledge Management (pp. 51–56). IARIA. http://hdl.handle.net/20.500.12708/41458

2015

• 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
• 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
• Recommendations on a Knowledge Graph / Grad-Gyenge, L., Filzmoser, P., & Werthner, H. (2015). Recommendations on a Knowledge Graph. In MLRec 2015 : 1st International Workshop on Machine Learning Methods for Recommender Systems (pp. 13–20). http://hdl.handle.net/20.500.12708/56430
• Simulation of Robust PCA for Supervised Audio Outlier Detection / Brodinova, S., Ortner, T., Filzmoser, P., Zaharieva, M., & Breiteneder, C. (2015). Simulation of Robust PCA for Supervised Audio Outlier Detection. In Eighth International Workshop on Simulation: Book of Abstracts. International Workshop on Simulation, Vienna, Austria. http://hdl.handle.net/20.500.12708/56521 / Project: FAMOUS
• Evaluation of Robust PCA for Supervised Audio Outlier Detection / Brodinova, S., Ortner, T., Filzmoser, P., Zaharieva, M., & Breiteneder, C. (2015). Evaluation of Robust PCA for Supervised Audio Outlier Detection (CS-2015-2). http://hdl.handle.net/20.500.12708/38539 / Project: FAMOUS

2014

• Spreading Activation for Rating Estimation in Recommender Systems / Grad-Gyenge, L., Werthner, H., & Filzmoser, P. (2014). Spreading Activation for Rating Estimation in Recommender Systems. The 15th International Conference on Electronic Commerce and Web Technologies (EC-Web 2014), Munich, Germany, EU. http://hdl.handle.net/20.500.12708/85935
• 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
• 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
• Robust variable selection in linear regression with compositional explanatory variables / Schroeder, F., Braumann, A., Filzmoser, P., & Hron, K. (2013). Robust variable selection in linear regression with compositional explanatory variables. In K. Hron, P. Filzmoser, & M. Templ (Eds.), Proceedings of the 5th International Workshop on Compositional Data Analysis CoDaWork 2013 June 3-7, 2013, Vorau, Austria (p. 55). http://hdl.handle.net/20.500.12708/41259

2012

• Robust variable selection for linear regression models with compositional data / Schroeder, F., Braumann, A., & Filzmoser, P. (2012). Robust variable selection for linear regression models with compositional data. Statistische Woche 2012, TU Wien, Austria. http://hdl.handle.net/20.500.12708/120300
• A generic model for the integration of interactive visualization and statistical computing using R / Kehrer, J., Boubela, R. N., Filzmoser, P., & Piringer, H. (2012). A generic model for the integration of interactive visualization and statistical computing using R. In 2012 IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE Conference on Visual Analytics Science and Technology, VAST 2012, Seattle, WA, USA, Non-EU. https://doi.org/10.1109/vast.2012.6400537

2011

• Uncertainty-Aware Exploration of Continuous Parameter Spaces Using Multivariate Prediction / Berger, W., Piringer, H., Filzmoser, P., & Gröller, E. (2011). Uncertainty-Aware Exploration of Continuous Parameter Spaces Using Multivariate Prediction. Computer Graphics Forum, 30(3), 911–920. http://hdl.handle.net/20.500.12708/164792
• Uncertainty-Aware Exploration of Continuous Parameter Spaces Using Multivariate Prediction / Berger, W., Piringer, H., Filzmoser, P., & Gröller, E. (2011). Uncertainty-Aware Exploration of Continuous Parameter Spaces Using Multivariate Prediction. EuroVis 2011, Bergen, Norway, EU. http://hdl.handle.net/20.500.12708/85231

2010

• Brushing Moments in Interactive Visual Analysis / Kehrer, J., Filzmoser, P., & Hauser, H. (2010). Brushing Moments in Interactive Visual Analysis. Eurograhics Digital Library, 29(3), 10. http://hdl.handle.net/20.500.12708/167123

2009

• Visplore and R: a symbiosis of powerfull visualization and statistical processing / Boubela, R., Filzmoser, P., & Piringer, H. (2009). Visplore and R: a symbiosis of powerfull visualization and statistical processing. Young Statisticians Meeting, Piran, Slowenien, EU. http://hdl.handle.net/20.500.12708/119215
• Integrating R into the InfoVis System Visplore / Boubela, R., Filzmoser, P., & Piringer, H. (2009). Integrating R into the InfoVis System Visplore. useR! 2009, Rennes, EU. http://hdl.handle.net/20.500.12708/119208

2008

• Integration der Statistiksoftware R in die Visualisierungssoftware Bulk Analyzer zur interaktiven Datenanalyse / Boubela, R., Filzmoser, P., & Piringer, H. (2008). Integration der Statistiksoftware R in die Visualisierungssoftware Bulk Analyzer zur interaktiven Datenanalyse. In Abstracts of the Workshop TU Wien/TU Dresden (p. 8). http://hdl.handle.net/20.500.12708/40781

Note: Due to the rollout of TU Wien’s new publication database, the list below may be slightly outdated. Once the migration is complete, everything will be up to date again.

• Outlier detection for mixed-attribute data / Priselac, S. (2022). Outlier detection for mixed-attribute data [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.99623
• Simulating the impact of a bonus-malus system in casco car insurance with Markov chains / Böhm, F. (2022). Simulating the impact of a bonus-malus system in casco car insurance with Markov chains [Diploma Thesis, Technische Universität Wien]. reposiTUm. http://hdl.handle.net/20.500.12708/79230
• Methods and applications for the secondary use of claims data from the Austrian health insurance system / Endel, F. (2021). Methods and applications for the secondary use of claims data from the Austrian health insurance system [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2021.35400
• Visual analysis of periodic time series data : supporting model selection, prediction, imputation, and outlier detection using visual analytics / Bögl, M. (2020). Visual analysis of periodic time series data : supporting model selection, prediction, imputation, and outlier detection using visual analytics [Dissertation, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2020.86301
• Performance of survival models on predictive maintenance of construction machines : semiparametric evaluation of predictive models / Guimarães, D. (2019). Performance of survival models on predictive maintenance of construction machines : semiparametric evaluation of predictive models [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2019.69382
• Analysis and visualization of Vienna's parking enforcement data / Lehner, J. (2019). Analysis and visualization of Vienna’s parking enforcement data [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2019.59368
• Personalizing the Austrian inflation rate / Tvrdic, T. (2018). Personalizing the Austrian inflation rate [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2018.42121
• Dynamic log file analysis: an unsupervised cluster evolution approach for anomaly detection / Landauer, M. (2018). Dynamic log file analysis: an unsupervised cluster evolution approach for anomaly detection [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2018.50604
• Cost-based statistical methods for fraud-detection : prediction of never paying customers considering individual risk / Heiler, G. (2017). Cost-based statistical methods for fraud-detection : prediction of never paying customers considering individual risk [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2017.51005

• Talentförderungsprämie (Sektion "Wissenschaft") vom Land Oberösterreich
  2003 / Austria
• Mobilitätsstipendium der CA für hervorragende Dissertation
  2001 / Austria

Soon, this page will include additional information such as reference projects, activities as journal reviewer and editor, memberships in councils and committees, and other research activities.

Until then, please visit Peter Filzmoser’s research profile in TISS .