Schnur, Christopher ; Dorst, Tanja ; Deshmukh, Kapil ; Zimmer, Sarah ; Litzenburger, Philipp ; Schneider, Tizian ; Margies, Lennard ; Müller, Rainer ; Schütze, Andreas (2024)
PIA - A Concept for a Personal Information Assistant for Data Analysis and Machine Learning of Time-Continuous Data in Industrial Applications.
In: ing.grid : FAIR data management in engineering sciences, 2023, 1 (2)
doi: 10.26083/tuprints-00026427
Article, Secondary publication, Publisher's Version
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| Item Type: | Article |
|---|---|
| Type of entry: | Secondary publication |
| Title: | PIA - A Concept for a Personal Information Assistant for Data Analysis and Machine Learning of Time-Continuous Data in Industrial Applications |
| Language: | English |
| Date: | 25 March 2024 |
| Place of Publication: | Darmstadt |
| Year of primary publication: | 25 October 2023 |
| Place of primary publication: | Darmstadt |
| Publisher: | Universitäts- und Landesbibliothek Darmstadt |
| Journal or Publication Title: | ing.grid : FAIR data management in engineering sciences |
| Volume of the journal: | 1 |
| Issue Number: | 2 |
| Collation: | 19 Seiten |
| DOI: | 10.26083/tuprints-00026427 |
| Corresponding Links: | |
| Origin: | Secondary publication from TUjournals |
| Abstract: | A database with high-quality data must be given to fully use the potential of Artificial Intelligence (AI). Especially in small and medium-sized companies with little experience with AI, the underlying database quality is often insufficient. This results in an increased manual effort to process the data before using AI. In this contribution, the authors developed a concept to enable inexperienced users to perform a first data analysis project with machine learning and record data with high quality. The concept comprises three modules: accessibility of (meta)data and knowledge, measurement and data planning, and data analysis. Furthermore, the concept was implemented as a front-end demonstrator on the example of an assembly station and published on the GitHub platform for potential users to test and review the concept. |
| Uncontrolled Keywords: | machine learning, data analysis, measurement and data planning |
| Status: | Publisher's Version |
| URN: | urn:nbn:de:tuda-tuprints-264271 |
| Classification DDC: | 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering |
| Divisions: | 16 Department of Mechanical Engineering > Institute for Fluid Systems (FST) (since 01.10.2006) > Research Data Management and Digital Literacy |
| Date Deposited: | 25 Mar 2024 13:30 |
| Last Modified: | 25 Mar 2024 14:30 |
| URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/26427 |
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