Authors: | Wiedau, Michael Tolksdorf, Gregor Oeing, Jonas Kockmann, Norbert |
Title: | Towards a systematic data harmonization to enable AI application in the process industry |
Language (ISO): | en |
Abstract: | Current methods of artificial intelligence may often proof ineffective in the process industry, usually because of insufficient data availability. In this contribution, we investigate how data standards can contribute to fulfill the data availability requirements of machine learning methods. We give an overview of AI use cases relevant in the process industry, name related requirements and discuss known standards in the context of implicit vs. explicit data. We conclude with a roadmap sketching how to bring the results of this contribution into practical application. |
Subject Headings: | Artificial intelligence Data integration KEEN project Ontology Process industry |
URI: | http://hdl.handle.net/2003/40765 http://dx.doi.org/10.17877/DE290R-22622 |
Issue Date: | 2021-11-15 |
Rights link: | https://creativecommons.org/licenses/by/4.0/ |
Appears in Collections: | Arbeitsgruppe Apparatedesign |
Files in This Item:
File | Description | Size | Format | |
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Chemie Ingenieur Technik - 2021 - Wiedau - Towards a Systematic Data Harmonization to Enable AI Application in the Process.pdf | DNB | 1.72 MB | Adobe PDF | View/Open |
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