DI Lisa Ehrlinger
Researcher Data Analysis Systems
Phone: +43 50 343 836
lisa.ehrlinger@scch.at
Data Science - DAS
L. Fischer, L. Ehrlinger, V. Geist, R. Ramler, F. Sobiezky, W. Zellinger, D. Brunner, M. Kumar, B. Moser. AI System Engineering - Key Challenges and Lessons Learned. Machine Learning & Knowledge Extraction, volume 3, number 1, pages 56-83, DOI: 10.3390/make3010004, December, 2020.
L. Fischer, L. Ehrlinger, V. Geist, R. Ramler, F. Sobieczky, W. Zellinger, B. Moser. Applying AI in practice: Key challenges and lessons learned. Machine Learning and Knowledge Extraction. CD-MAKE 2020, volume 12279, pages 451-471, DOI 10.1007/978-3-030-57321-8_25, Springer, August, 2020.
L. Ehrlinger, C. Lettner, J. Himmelbauer. Tackling semantic shift in industrial streaming data over time. In M. Crowe, L. Ehrlinger, F. Laux, A. Schmidt (editors), Proceedings of the 12th International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA 2020), pages 36-39, IARIA, October, 2020.
L. Ehrlinger, V. Haunschmid, D. Palazzini, C. Lettner. A DaQL to monitor data quality in machine learning applications. In S. Hartmann, J. Küng, S. Chakravarthy, G. Anderst-Kotsis, A. Tjoa, I. Khalil (editors), Database and Expert Systems Applications - Proc. DEXA 2019, Part II, Lecture Notes of Computer Science, volume 11709, pages 227-237, DOI 10.1007/978-3-030-27615-7_17, Springer, August, 2019.
L. Ehrlinger, W. Wöß. A novel data quality metric for minimality. In H. Hacid, Q. Sheng, T. Yoshida, A. Sarkheyli, R. Zhou (editors), Data Quality and Trust in Big Data – QUAT 2018 in conjunction with WISE 2018, Revised Selected Papers, Lecture Notes of Computer Science, volume 11235, pages 1-15, DOI 10.1007/978-3-030-19143-6_1, Springer, April, 2019.
L. Ehrlinger, G. Huszar, W. Wöß. A schema readability metric for automated data quality measurement. In F. Laux, L. Ehrlinger (editors), Proceedings of the 11th International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA 2019), pages 4-10, IARIA, June, 2019.
L. Ehrlinger, W. Wöß. Automated schema quality measurement in large-scale information systems. In H. Hacid, Q. Sheng, T. Yoshida, A. Sarkheyli, R. Zhou (editors), Data Quality and Trust in Big Data – QUAT 2018 in conjunction with WISE 2018, Revised Selected Papers, Lecture Notes of Computer Science, volume 11235, pages 16-31, DOI 10.1007/978-3-030-19143-6_2, Springer, April, 2019.
J. Borovina Josko, L. Ehrlinger, W. Wöß. Towards a knowledge graph to describe and process data defects. In F. Laux, L. Ehrlinger (editors), Proceedings of the 11th International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA 2019), pages 57-60, IARIA, June, 2019.
L. Ehrlinger, T. Grubinger, B. Varga, M. Pichler, T. Natschläger, J. Zeindl. Treating missing data in Industrial Data Analytics. In P. Pichappan, A. Florea, M. Naeem (editors), Proceedings of the 13th International Conference on Digital Information Management (ICDIM 2018), pages 148-155, IEEE, September, 2018.
Proceedings 2020 IEEE 22nd Conference on Business Informatics (CBI 2020). In W. Guédria, H. Proper, J. Verelst, S. Hacks, F. Timm, K. Sandkuhl, M. Fellmann, G. Serapiao, M. Payan, M. Komarov, S. Maltseva, R. Uskenbayeva, D. Nazarov, M. Ge, M. Helfert, L. Ehrlinger (editors), DOI 10.1109/CBI49978.2020, IEEE, June, 2020.
L. Ehrlinger, C. Lettner, J. Himmelbauer. Tackling semantic shift in industrial streaming data over time. In M. Crowe, L. Ehrlinger, F. Laux, A. Schmidt (editors), Proceedings of the 12th International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA 2020), pages 36-39, IARIA, October, 2020.
