Sabrina Meindl MSc
Publikationen
-
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.
-
NADA: NMF-based Anomaly Detection in Adjacency-Matrices for Industrial Machine Log-files
Luftensteiner, S., Praher, P., & Schwarz, N. (2024). NADA: NMF-Based Anomaly Detection in Adjacency-Matrices for Industrial Machine Log-Files. Big Data Analytics and Knowledge Discovery, 369–374.
-
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.