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A data model and architecture for long-term preservation
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International Conference on Digital Libraries archive
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries table of contents
Pittsburgh PA, PA, USA
SESSION: Preservation and archiving table of contents
Pages: 134-144  
Year of Publication: 2008
ISBN:978-1-59593-998-2
Authors
Greg Janée  University of California, Santa Barbara, Santa Barbara, CA, USA
Justin Mathena  University of California, Santa Barbara, Santa Barbara, CA, USA
James Frew  University of California, Santa Barbara, Santa Barbara, CA, USA
Sponsors
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SIGIR: ACM Special Interest Group on Information Retrieval
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

The National Geospatial Digital Archive, one of eight initial projects funded under the Library of Congress's NDIIPP program, has been researching how geospatial data can be preserved on a national scale and be made available to future generations. In this paper we describe an archive architecture that provides a minimal approach to the long-term preservation of digital objects based on co-archiving of object semantics, uniform representation of objects and semantics, explicit storage of all objects and semantics as files, and abstraction of the underlying storage system. This architecture ensures that digital objects can be easily migrated from archive to archive over time and that the objects can, in principle, be made usable again at any point in the future; its primary benefit is that it serves as a fallback strategy against, and as a foundation for, more sophisticated (and costly) preservation strategies. We describe an implementation of this architecture in a protoype archive running at UCSB that also incorporates a suite of ingest and access components.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Greg Janée: colleagues
Justin Mathena: colleagues
James Frew: colleagues