A Framework for Factory-Trained Virtual Sensor Models Based on Censored Production Data. - Sabrina Luftensteiner; Michael Zwick (DEXA (2), 2020)

Filter-Based Feature Selection Methods for Industrial Sensor Data: A Review. - Sabrina Luftensteiner; Michael Mayr; Georgios C. Chasparis (DaWaK, 2021)

Generalized Input-Output Hidden-Markov-Models for Supervising Industrial Processes. - Georgios C. Chasparis; Sabrina Luftensteiner; Michael Mayr (ISM, 2021)

Abstracting Process Mining Event Logs From Process-State Data To Monitor Control-Flow Of Industrial Manufacturing Processes. - Michael Mayr; Sabrina Luftensteiner; Georgios C. Chasparis (ISM, 2021)

From Data to Decisions - Developing Data Analytics Use-Cases in Process Industry. - Johannes Himmelbauer; Michael Mayr; Sabrina Luftensteiner (DEXA Workshops, 2022)

Log File Anomaly Detection Based on Process Mining Graphs. - Sabrina Luftensteiner; Patrick Praher (DEXA Workshops, 2022)

A Synthetic Dataset for Anomaly Detection of Machine Behavior. - Sabrina Luftensteiner; Patrick Praher (DEXA Workshops, 2022)

Gathering Expert Knowledge in Process Industry. - Sabrina Luftensteiner; Georgios C. Chasparis; Michael Mayr (ISM, 2022)

Improving Virtual Sensor Models by Censored Online Data. - Sabrina Luftensteiner; Michael Zwick (ISM, 2022)

PAS - A Feature Selection Process Definition for Industrial Settings. - Sabrina Luftensteiner; Georgios C. Chasparis; Josef Küng (ISM, 2023)

NADA: NMF-Based Anomaly Detection in Adjacency-Matrices for Industrial Machine Log-Files. - Sabrina Luftensteiner; Patrick Praher; Nicole Schwarz (DaWaK, 2024)