Version 1
: Received: 2 September 2024 / Approved: 2 September 2024 / Online: 2 September 2024 (08:53:49 CEST)
How to cite:
Johnson, W.; Davis, J.; Kelly, T. Redefining Data-Centric Design: A New Approach with a Domain Model and Core Data Ontology for Computational Systems. Preprints2024, 2024090037. https://doi.org/10.20944/preprints202409.0037.v1
Johnson, W.; Davis, J.; Kelly, T. Redefining Data-Centric Design: A New Approach with a Domain Model and Core Data Ontology for Computational Systems. Preprints 2024, 2024090037. https://doi.org/10.20944/preprints202409.0037.v1
Johnson, W.; Davis, J.; Kelly, T. Redefining Data-Centric Design: A New Approach with a Domain Model and Core Data Ontology for Computational Systems. Preprints2024, 2024090037. https://doi.org/10.20944/preprints202409.0037.v1
APA Style
Johnson, W., Davis, J., & Kelly, T. (2024). Redefining Data-Centric Design: A New Approach with a Domain Model and Core Data Ontology for Computational Systems. Preprints. https://doi.org/10.20944/preprints202409.0037.v1
Chicago/Turabian Style
Johnson, W., James Davis and Tara Kelly. 2024 "Redefining Data-Centric Design: A New Approach with a Domain Model and Core Data Ontology for Computational Systems" Preprints. https://doi.org/10.20944/preprints202409.0037.v1
Abstract
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.
Keywords
Data-centric design; distributed data ecosystems; data security; semantic interoperability; ontology development
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
