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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.09058 |
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| _version_ | 1866909316208918528 |
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| author | Johnson, William Davis, James Kelly, Tara |
| author_facet | Johnson, William Davis, James Kelly, Tara |
| contents | This paper presents an innovative data-centric paradigm for designing computational systems by introducing a new informatics domain model. The proposed model moves away from the conventional node-centric framework and focuses on data-centric categorization, using a multimodal approach that incorporates objects, events, concepts, and actions. By drawing on interdisciplinary research and establishing a foundational ontology based on these core elements, the model promotes semantic consistency and secure data handling across distributed ecosystems. We also explore the implementation of this model as an OWL 2 ontology, discuss its potential applications, and outline its scalability and future directions for research. This work aims to serve as a foundational guide for system designers and data architects in developing more secure, interoperable, and scalable data systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09058 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Redefining Data-Centric Design: A New Approach with a Domain Model and Core Data Ontology for Computational Systems Johnson, William Davis, James Kelly, Tara Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning This paper presents an innovative data-centric paradigm for designing computational systems by introducing a new informatics domain model. The proposed model moves away from the conventional node-centric framework and focuses on data-centric categorization, using a multimodal approach that incorporates objects, events, concepts, and actions. By drawing on interdisciplinary research and establishing a foundational ontology based on these core elements, the model promotes semantic consistency and secure data handling across distributed ecosystems. We also explore the implementation of this model as an OWL 2 ontology, discuss its potential applications, and outline its scalability and future directions for research. This work aims to serve as a foundational guide for system designers and data architects in developing more secure, interoperable, and scalable data systems. |
| title | Redefining Data-Centric Design: A New Approach with a Domain Model and Core Data Ontology for Computational Systems |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2409.09058 |