Towards self-validating knowledge-based archives

B. Ludäscher, R. Marciano, R. Moore

Research output: Contribution to conferencePaperpeer-review

Abstract

Digital archives are dedicated to the long-term preservation of electronic information and have the mandate to enable sustained access despite a rapidly changing information infrastructure. Current archival approaches build upon standardized data formats and simple metadata mechanisms for collection management, but do not involve high-level conceptual models and knowledge representations. This results in serious limitations, not only for expressing various kinds of information and knowledge about the archived data, but also for creating infrastructure independent, self-validating and self-instantiating archives. To overcome these limitations, we first propose a scalable XML-based archival infrastructure, based on standard tools, and subsequently show how this architecture can be extended to a model-based framework, where higher-level knowledge representations become an integral part of the archive and the ingestion/migration processes. This allows us to maximize infrastructure independence by archiving generic, executable specifications of (i) archival constraints (i.e., "model validators"), and (ii) archival transformations that are part of the ingestion process. The proposed architecture facilitates construction of self-validating and self-instantiating knowledge-based archives. We illustrate our overall approach and report on first experiences using a sample collection from a collaboration with the National Archives and Records Administration (NARA).

Original languageEnglish (US)
Pages9-16
Number of pages8
StatePublished - Jan 1 2001
Externally publishedYes
Event11th International Workshop on Research Issues in Data Engineering (RIDE 2001) - Heidelberg, Germany
Duration: Apr 1 2001Apr 2 2001

Other

Other11th International Workshop on Research Issues in Data Engineering (RIDE 2001)
CountryGermany
CityHeidelberg
Period4/1/014/2/01

ASJC Scopus subject areas

  • Computer Science(all)

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