DI (FH) Michael Zwick

Researcher Data Analysis Systems
Phone: +43 50 343 843


Data Science - DAS

S. Luftensteiner, M. Zwick. A framework for factory-frained virtual sensor models based on censored production data. In S. Hartmann, J. Küng, G. Kotsis, A. Tjoa, I. Khalil (editors), DEXA 2020: Database and Expert Systems Applications, Part II, Lecture Notes in Computer Science, volume 12392, pages 3-16, DOI 10.1007/978-3-030-59051-2_1, Springer, September, 2020.

W. Zellinger, T. Grubinger, M. Zwick, E. Lughofer, H. Schöner, T. Natschläger, S. Saminger-Platz. Multi-source transfer learning of time series in cyclical manufacturing. Journal of Intelligent Manufacturing, volume 31, pages 777-787, DOI: 10.1007/s10845-019-01499-4, March, 2020.

S. Luftensteiner, M. Zwick, G. Chasparis, S. Brayer, T. Grubinger. A framework for improving offline learning models with online data. Technical Report, number SCCH-TR-19021, September, 2019.

F. Kossak, M. Zwick. ML-PipeDebugger: A debugging tool for data processing pipelines?. In S. Hartmann, J. Küng, S. Chakravarthy, G. Anderst-Kotsis, A. Tjoa, I. Khalil Ibrahim (editors), Database and Expert Systems Applications - Proc. DEXA 2019, Part II, Lecture Notes of Computer Science, volume 11707, pages 263-272, DOI 10.1007/978-3-030-27618-8_20, Springer, August, 2019.

W. Zellinger, H. Eghbal-zadeh, B. Moser, M. Zwick, E. Lughofer, T. Natschläger, S. Saminger-Platz. Moment distances for comparing high-entropy. BoA of the International Conference on Computational Statistics (COMPSTAT 2018), August, 2018.

T. Fernando, N. Gureev, M. Zwick, T. Natschläger, M. Matskin. WorkflowDSL: Scalable workflow execution with provenance for data analysis applictions. Proceedings of the 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC 2018), pages 774-779, DOI 10.1109/COMPSAC.2018.00115, IEEE, July, 2018.

W. Zellinger, M. Zwick, T. Grubinger. Domain-invariant feature learning with recurrent autoencoders for time series prediction. Technical Report, number SCCH-TR-17050, August, 2017.

W. Zellinger, M. Zwick, T. Grubinger. Domain-invariant representation learning for metal sheet bending. Technical Report, number SCCH-TR-17051, August, 2017.

T. Grubinger, M. Zwick. Multi-task learning for scenario invariant time series data. Technical Report, number SCCH-TR-17052, August, 2017.

C. Lettner, M. Zwick. A data analysis framework for high-variety product lines in the industrial manufacturing domain. In S. Hammoudi, L. Maciaszek, J. Cordeiro (editors), Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS 2014), volume 1, pages 209-216, April, 2014.

T. Steinmaurer, P. Traxler, M. Zwick, R. Stumptner, L. Christian. Combining stream processing engines and big data storages for data analysis. In T. Andreasen, H. Christiansen, J. Cubero, Z. Ras (editors), Foundations of Intelligent Systems - Proc. ISMIS 2014, Lecture Notes in Computer Science, volume 8502, pages 476-485, Springer, June, 2014.

T. Grubinger, M. Roßbory, H. Schöner, M. Zwick. ModControl Springback. Technical Report, June, 2014.

M. Zwick, H. Schöner, S. F.Rezaie. SymReg-MT: Iterative multi-task feature learning through weighted symbolic regression. LMCE 2014 - 1st International Workshop on Learning over Multiple Context, Nancy, France, September 19, 2014, pages http://users.dsic.upv.es/~flip/LMCE2014/Papers/lmce2014_submission_14.pdf, September, 2014.

M. Zwick, C. Lettner, C. Hawel. Implementing automated analyses in an active data warehouse environment using workflow technology. In D. Draheim, G. Weber (editors), Trends in Enterprise Application Architecture, Lecture Notes in Computer Science, volume 4473, pages 341-354, Springer, 2007.