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Long-term preservation of product data is imperative for many organizations. A product data archive should be designed to ensure information accessibility and understanding over time. Approaches such as the Open Archival Information System (OAIS) Reference Model and the Audit and Certi cation of Trustworthy Digital Repositories (ACTDR)provide a framework for conceptually describing and evaluating archives. These approaches are generic and do not focus on particular contexts or content types, such as product data. Moreover, these recommendations offer no guidance on how to formally and comprehensively describe archival systems. Such descriptions should include the business activities that a product data archive has to support, and the systems that interact with the archive. Enterprise architecture provides a means to describe systems in their potentially complex environments. This paper proposes a holistic approach to formally describe the architecture and the environment of archival systems. This approach relies on the formal representation of the preservation terminology, including OAIS concepts, using the Department of Defense Architecture Framework (DoDAF). The approach covers the various interactions of other business functions with the archive, and the information models necessary to ensure preservation and accessibility of product data. This approach is a step toward a reference architecture for the formal description of archival systems. To demonstrate the approach, we formally describe the ingest of product data related to a ship. The resulting description uses the preservation terminology de ned in the OAIS Reference Model. It facilitates the understanding of how the preservation solution is actually implemented, and provides evidence that the solution is able to preserve product data and make it accessible.
Citation
Journal of Computing and Information Science in Engineering
Barbau, R.
, Lubell, J.
, Rachuri, S.
and Foufou, S.
(2014),
Towards a Reference Architecture for Archival Systems: Use Case With Product Data, Journal of Computing and Information Science in Engineering, [online], https://doi.org/10.1115/1.4027150
(Accessed October 7, 2025)