TY - GEN
T1 - A Metadata Based Equipment Integrated Logistics Support Data Ontology Modeling Method
AU - Cui, Xuemiao
AU - Pan, Chen
AU - Lu, Jiping
AU - Han, Yafeng
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Equipment integrated logistics support (ILS) data modeling is the foundation for equipment ILS design, analysis, and decision-making. At present, the ILS data resources have the characteristics of multi-source heterogeneous and complex association. Traditional data modeling methods cannot clearly express the property information and relationship of ILS data. Therefore, based on metadata and ontology theory, this paper proposes a four-level ILS data modeling framework to solve the unified expression problem of ILS data. Firstly, the ILS metadata schemata are summarized and defined. Secondly, the construction process of ILS core metadata ontology and specialized metadata ontology is analyzed. Then, the ontology model and linked data model are constructed to express the relationships between ILS data and product hierarchy, operation event, lifecycle stage, and data category. Finally, the ILS linked data modeling and model information retrieval operations are carried out through the developed ILS data modeling software, which verifies the feasibility of the proposed method.
AB - Equipment integrated logistics support (ILS) data modeling is the foundation for equipment ILS design, analysis, and decision-making. At present, the ILS data resources have the characteristics of multi-source heterogeneous and complex association. Traditional data modeling methods cannot clearly express the property information and relationship of ILS data. Therefore, based on metadata and ontology theory, this paper proposes a four-level ILS data modeling framework to solve the unified expression problem of ILS data. Firstly, the ILS metadata schemata are summarized and defined. Secondly, the construction process of ILS core metadata ontology and specialized metadata ontology is analyzed. Then, the ontology model and linked data model are constructed to express the relationships between ILS data and product hierarchy, operation event, lifecycle stage, and data category. Finally, the ILS linked data modeling and model information retrieval operations are carried out through the developed ILS data modeling software, which verifies the feasibility of the proposed method.
KW - data modeling
KW - integrated logistics support
KW - metadata
KW - ontology
KW - software implementation
UR - http://www.scopus.com/inward/record.url?scp=85135384555&partnerID=8YFLogxK
U2 - 10.1109/CVIDLICCEA56201.2022.9824219
DO - 10.1109/CVIDLICCEA56201.2022.9824219
M3 - Conference contribution
AN - SCOPUS:85135384555
T3 - 2022 3rd International Conference on Computer Vision, Image and Deep Learning and International Conference on Computer Engineering and Applications, CVIDL and ICCEA 2022
SP - 144
EP - 148
BT - 2022 3rd International Conference on Computer Vision, Image and Deep Learning and International Conference on Computer Engineering and Applications, CVIDL and ICCEA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Computer Vision, Image and Deep Learning and International Conference on Computer Engineering and Applications, CVIDL and ICCEA 2022
Y2 - 20 May 2022 through 22 May 2022
ER -