Dr. Michael Mayr

Researcher and Senior Data Scientist

Publikationen

2026
Topologically-Constrained Any-Variate Time-Series Foundation Models for Twinning of Continuous Industrial Processes

Mayr, M., & Chasparis, G. C.. (2026). Topologically-Constrained Any-Variate Time-Series Foundation Models for Twinning of Continuous Industrial Processes. In Lecture Notes in Computer Science (pp. 189–196). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-34896-8_15
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2025
Causally-Guided Pairwise Transformer - Towards Foundational Digital Twins in Process Industry

Mayr, M., & Chasparis, G. C.. (2026). Causally-Guided Pairwise Transformer - Towards Foundational Digital Twins in Process Industry. In Procedia Computer Science (pp. 175–184). Elsevier BV. https://doi.org/10.1016/j.procs.2026.02.059
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Automated Knowledge Graph Learning in Industrial Processes

Ammann, L., Martinez-Gil, J., Mayr, M., & Chasparis, G. C. (2025). Automated Knowledge Graph Learning in Industrial Processes. Procedia Computer Science, 253, 2428–2437.
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2024
Database and Expert Systems Applications - DEXA 2024 Workshops

Database and Expert Systems Applications - DEXA 2024 Workshops. (2024). In B. Moser, L. Fischer, A. Mashkoor, J. Sametinger, A.-C. Glock, M. Mayr, & S. Luftensteiner (Eds.), Communications in Computer and Information Science. Springer Nature Switzerland.
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Learning Paradigms and Modelling Methodologies for Digital Twins in Process Industry

Mayr, M., Chasparis, G. C., & Küng, J. (2024). Learning Paradigms and Modelling Methodologies for Digital Twins in Process Industry. Big Data Analytics and Knowledge Discovery, 34–47.
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2023
Opening the black-box of Neighbor Embeddings with Hotelling’s T2 statistic and Q-residuals

Rainer, R. J., Mayr, M., Himmelbauer, J., & Nikzad-Langerodi, R. (2023). Opening the black-box of Neighbor Embeddings with Hotelling’s T2 statistic and Q-residuals. In Chemometrics and Intelligent Laboratory Systems (Vol. 238, p. 104840). Elsevier BV.
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Gathering Expert Knowledge in Process Industry

Luftensteiner, S., Chasparis, G. C., & Mayr, M. (2023). Gathering Expert Knowledge in Process Industry. Procedia Computer Science, 217, 960–968.
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2022
From Data to Decisions - Developing Data Analytics Use-Cases in Process Industry

Himmelbauer, J., Mayr, M., & Luftensteiner, S. (2022). From Data to Decisions - Developing Data Analytics Use-Cases in Process Industry. Database and Expert Systems Applications - DEXA 2022 Workshops, 79–89.
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Abstracting Process Mining Event Logs From Process-State Data To Monitor Control-Flow Of Industrial Manufacturing Processes

Mayr, M., Luftensteiner, S., & Chasparis, G. C. (2022). Abstracting Process Mining Event Logs From Process-State Data To Monitor Control-Flow Of Industrial Manufacturing Processes. Procedia Computer Science, 200, 1442–1450.
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Generalized Input-Output Hidden-Markov-Models for Supervising Industrial Processes

Chasparis, G. C., Luftensteiner, S., & Mayr, M. (2022). Generalized Input-Output Hidden-Markov-Models for Supervising Industrial Processes. Procedia Computer Science, 200, 1402–1411.
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2021
Filter-based Feature Selection Methods for Industrial Sensor Data: A Review

Luftensteiner, S., Mayr, M., & Chasparis, G. (2021). Filter-Based Feature Selection Methods for Industrial Sensor Data: A Review. Lecture Notes in Computer Science, 242–249.
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AVUBDI: A Versatile Usable Big Data Infrastructure and its Monitoring Approaches for Process Industry

Luftensteiner, S., Mayr, M., Chasparis, G. C., & Pichler, M. (2021). AVUBDI: A Versatile Usable Big Data Infrastructure and Its Monitoring Approaches for Process Industry. Frontiers in Chemical Engineering, 3.
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2020
Detecting anomalies in production quality data using a method based on The Chi-Square Test Statistic

Mayr, M., & Himmelbauer, J. (2020). Detecting Anomalies in Production Quality Data Using a Method Based on the Chi-Square Test Statistic. Lecture Notes in Computer Science, 348–363.
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2019
A framework for unsupervised anomaly detection in production quality data

Mayr, M. (2019). A framework for unsupervised anomaly detection in production quality data. Master thesis, FH OÖ, Fachhochschul-Masterstudiengang Data Science and Engineering, September 2019.
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