Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Product Lifecycle Management. Leveraging Digital Twins, Circular Economy, and Knowledge Management for Sustainable Innovation - 20th IFIP WG 5.1 International Conference, PLM 2023, Revised Selected Papers |
| Herausgeber/-innen | Christophe Danjou, Ramy Harik, Felix Nyffenegger, Louis Rivest, Abdelaziz Bouras |
| Herausgeber (Verlag) | Springer Science and Business Media Deutschland GmbH |
| Seiten | 116-126 |
| Seitenumfang | 11 |
| ISBN (Print) | 9783031625770 |
| Publikationsstatus | Veröffentlicht - 28 Juni 2024 |
| Veranstaltung | 20th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2023 - Montreal, Kanada Dauer: 9 Juli 2023 → 12 Juli 2023 |
Publikationsreihe
| Name | IFIP Advances in Information and Communication Technology |
|---|---|
| Band | 701 IFIPAICT |
| ISSN (Print) | 1868-4238 |
| ISSN (elektronisch) | 1868-422X |
Abstract
The digitization of technologies in product manufacturing results in the availability of large amounts of process and product data. To gain knowledge from this data and fully leverage its potential, its structuring and semantically annotation is essential. This allows preserving the context of data generation and makes the data machine-readable and interpretable. Contextualization is the key to generating FAIR (Findable, Accessible, Interoperable, Reusable) data. The documentation of research activities and provenance of generated data is usually achieved by protocols. However, there is often a tension between the desire to document data generation in a structured, semantically rich form and the need to design research and process parameters flexibly as experimental conditions change. To resolve these contradictions, a dynamic model is described that allows to document research activities and implemented into a knowledge and research data management system to resolve these contradictions. The model allows a formal, semantic representation of research steps, parameters and gathered data, while also providing flexibility in the generation of protocol templates and individual experiments through the reuse of semantic building blocks. The approach is carried out within the context of a large collaborative research center, showcasing its use in managing and providing data for heterogeneous research tasks, documentation, and data types across interdisciplinary projects.
ASJC Scopus Sachgebiete
- Entscheidungswissenschaften (insg.)
- Informationssysteme und -management
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
Product Lifecycle Management. Leveraging Digital Twins, Circular Economy, and Knowledge Management for Sustainable Innovation - 20th IFIP WG 5.1 International Conference, PLM 2023, Revised Selected Papers. Hrsg. / Christophe Danjou; Ramy Harik; Felix Nyffenegger; Louis Rivest; Abdelaziz Bouras. Springer Science and Business Media Deutschland GmbH, 2024. S. 116-126 (IFIP Advances in Information and Communication Technology; Band 701 IFIPAICT).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Contextualization for Generating FAIR Data
T2 - 20th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2023
AU - Altun, Osman
AU - Hinterthaner, Marc
AU - Barienti, Khemais
AU - Nürnberger, Florian
AU - Lachmayer, Roland
AU - Mozgova, Iryna
AU - Koepler, Oliver
AU - Auer, Sören
N1 - Publisher Copyright: © IFIP International Federation for Information Processing 2024.
PY - 2024/6/28
Y1 - 2024/6/28
N2 - The digitization of technologies in product manufacturing results in the availability of large amounts of process and product data. To gain knowledge from this data and fully leverage its potential, its structuring and semantically annotation is essential. This allows preserving the context of data generation and makes the data machine-readable and interpretable. Contextualization is the key to generating FAIR (Findable, Accessible, Interoperable, Reusable) data. The documentation of research activities and provenance of generated data is usually achieved by protocols. However, there is often a tension between the desire to document data generation in a structured, semantically rich form and the need to design research and process parameters flexibly as experimental conditions change. To resolve these contradictions, a dynamic model is described that allows to document research activities and implemented into a knowledge and research data management system to resolve these contradictions. The model allows a formal, semantic representation of research steps, parameters and gathered data, while also providing flexibility in the generation of protocol templates and individual experiments through the reuse of semantic building blocks. The approach is carried out within the context of a large collaborative research center, showcasing its use in managing and providing data for heterogeneous research tasks, documentation, and data types across interdisciplinary projects.
AB - The digitization of technologies in product manufacturing results in the availability of large amounts of process and product data. To gain knowledge from this data and fully leverage its potential, its structuring and semantically annotation is essential. This allows preserving the context of data generation and makes the data machine-readable and interpretable. Contextualization is the key to generating FAIR (Findable, Accessible, Interoperable, Reusable) data. The documentation of research activities and provenance of generated data is usually achieved by protocols. However, there is often a tension between the desire to document data generation in a structured, semantically rich form and the need to design research and process parameters flexibly as experimental conditions change. To resolve these contradictions, a dynamic model is described that allows to document research activities and implemented into a knowledge and research data management system to resolve these contradictions. The model allows a formal, semantic representation of research steps, parameters and gathered data, while also providing flexibility in the generation of protocol templates and individual experiments through the reuse of semantic building blocks. The approach is carried out within the context of a large collaborative research center, showcasing its use in managing and providing data for heterogeneous research tasks, documentation, and data types across interdisciplinary projects.
KW - Data Management
KW - FAIR Data
KW - Knowledge Management
KW - Semantic Annotation
UR - http://www.scopus.com/inward/record.url?scp=85199581013&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-62578-7_11
DO - 10.1007/978-3-031-62578-7_11
M3 - Conference contribution
AN - SCOPUS:85199581013
SN - 9783031625770
T3 - IFIP Advances in Information and Communication Technology
SP - 116
EP - 126
BT - Product Lifecycle Management. Leveraging Digital Twins, Circular Economy, and Knowledge Management for Sustainable Innovation - 20th IFIP WG 5.1 International Conference, PLM 2023, Revised Selected Papers
A2 - Danjou, Christophe
A2 - Harik, Ramy
A2 - Nyffenegger, Felix
A2 - Rivest, Louis
A2 - Bouras, Abdelaziz
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 9 July 2023 through 12 July 2023
ER -