Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Lisa Ehrlinger"'
Publikováno v:
Frontiers in Big Data, Vol 6 (2023)
Externí odkaz:
https://doaj.org/article/a5cac48eb65a47b296fd4fb38bb79b7b
Publikováno v:
IEEE Access, Vol 9, Pp 55537-55554 (2021)
Knowledge graphs in manufacturing and production aim to make production lines more efficient and flexible with higher quality output. This makes knowledge graphs attractive for companies to reach Industry 4.0 goals. However, existing research in the
Externí odkaz:
https://doaj.org/article/63ab2212a6eb4828a71c78529a8ee8e9
Autor:
Lukas Fischer, Lisa Ehrlinger, Verena Geist, Rudolf Ramler, Florian Sobiezky, Werner Zellinger, David Brunner, Mohit Kumar, Bernhard Moser
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 3, Iss 1, Pp 56-83 (2020)
The main challenges are discussed together with the lessons learned from past and ongoing research along the development cycle of machine learning systems. This will be done by taking into account intrinsic conditions of nowadays deep learning models
Externí odkaz:
https://doaj.org/article/1df75bcbb392435e84922f21b61f9403
Autor:
Lisa Ehrlinger, Wolfram Wöß
Publikováno v:
Frontiers in Big Data, Vol 5 (2022)
High-quality data is key to interpretable and trustworthy data analytics and the basis for meaningful data-driven decisions. In practical scenarios, data quality is typically associated with data preprocessing, profiling, and cleansing for subsequent
Externí odkaz:
https://doaj.org/article/5dc6c3288b084d858352602d1aba0d81
Autor:
Bernhard Moser, Florian Sobiezky, Werner Zellinger, Rudolf Ramler, Lisa Ehrlinger, Mohit Kumar, David Brunner, Lukas Fischer, Verena Geist
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 3, Iss 4, Pp 56-83 (2021)
The main challenges are discussed together with the lessons learned from past and ongoing research along the development cycle of machine learning systems. This will be done by taking into account intrinsic conditions of nowadays deep learning models
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b5ba44b21ee640f6248b39785c481c7d
https://www.mdpi.com/2504-4990/3/1/4
https://www.mdpi.com/2504-4990/3/1/4
Publikováno v:
IEEE Access, Vol 9, Pp 55537-55554 (2021)
Knowledge graphs in manufacturing and production aim to make production lines more efficient and flexible with higher quality output. This makes knowledge graphs attractive for companies to reach Industry 4.0 goals. However, existing research in the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c985575e59c137a955b6a7a030ea4a22
https://ieeexplore.ieee.org/document/9393345/
https://ieeexplore.ieee.org/document/9393345/
Publikováno v:
Procedia Computer Science. 180:772-777
In order to make good decisions, the data used for decision-making needs to be of high quality. As the volume of data continually increases, ensuring high data quality is a big challenge nowadays and needs to be automated with tools. The goal of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::21cc787392bf7d8e75440603112e1005
https://doi.org/10.1016/j.procs.2021.01.327
https://doi.org/10.1016/j.procs.2021.01.327
Publikováno v:
Communications in Computer and Information Science ISBN: 9783031143427
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aa70db0806fa1a84c5633d78763b16c7
https://doi.org/10.1007/978-3-031-14343-4_34
https://doi.org/10.1007/978-3-031-14343-4_34
Autor:
Lisa Ehrlinger, Christian Lettner, Werner Fragner, Günter Gsellmann, Susanne Nestelberger, Franz Rauchenzauner, Stefan Schützeneder, Martin Tiefengrabner, Jürgen Zeindl
Publikováno v:
Communications in Computer and Information Science ISBN: 9783031143427
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::823f12e1e1e8ee635c18ba83cb5e9ca5
https://doi.org/10.1007/978-3-031-14343-4_16
https://doi.org/10.1007/978-3-031-14343-4_16
Publikováno v:
Computer Aided Systems Theory – EUROCAST 2022 ISBN: 9783031253119
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3aea0bdb0232216fc02e046e26114b84
https://doi.org/10.1007/978-3-031-25312-6_71
https://doi.org/10.1007/978-3-031-25312-6_71