Sabrina Meindl MSc

Researcher and Senior Data Scientist

Publikationen

2024
PAS - A Feature Selection Process Definition for Industrial Settings

Luftensteiner, S., Chasparis, G. C., & Küng, J. (2024). PAS - A Feature Selection Process Definition for Industrial Settings. Procedia Computer Science, 232, 308–316.
2023
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.
Improving Offline Deep Learning Models by Censored Online Data

Luftensteiner, S., & Zwick, M. (2023). Improving Virtual Sensor Models by Censored Online Data. Procedia Computer Science, 217, 938–947.
2022
A Synthetic Dataset for Anomaly Detection of Machine Behavior

Luftensteiner, S., & Praher, P. (2022). A Synthetic Dataset for Anomaly Detection of Machine Behavior. Database and Expert Systems Applications - DEXA 2022 Workshops, 424–431.
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.
Log File Anomaly detection based on Process Mining Graphs

Luftensteiner, S., & Praher, P. (2022). Log File Anomaly Detection Based on Process Mining Graphs. Database and Expert Systems Applications - DEXA 2022 Workshops, 383–391.
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.
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.
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.
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.
A Framework for Improving Offline Learning Models with Online Data

Luftensteiner, S., & Zwick, M. (2021) A Framework for Improving Offline Learning Models with Online Data. The Thirteen International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA 2021)
2020
A framework for factory-trained virtual sensor models based on censored production data

Luftensteiner, S., & Zwick, M. (2020). A Framework for Factory-Trained Virtual Sensor Models Based on Censored Production Data. Database and Expert Systems Applications, 3–16.
2019
Improving offline deep learning models by censored online data and quadratic programming

Luftensteiner, S. (2019) Improving offline deep learning models by censored online data and quadratic programming. Master thesis, FH OÖ, Fachhochschul-Masterstudiengang Software Engineering, July 2019.