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Related Experiment Videos

DataFoundry: information management for scientific data.

T Critchlow1, K Fidelis, M Ganesh

  • 1Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, CA 94550, USA.

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society
|April 13, 2000
PubMed
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Scientific data warehousing faces challenges due to complex schema integration and dynamic data. DataFoundry offers a novel solution for integrating scientific data, reducing maintenance costs for data warehouses.

Area of Science:

  • Data management in scientific research.
  • Information integration for scientific discovery.

Background:

  • Commercial data warehouses are effective but not directly applicable to scientific environments.
  • Scientific databases present unique challenges for data integration and warehousing.

Purpose of the Study:

  • To address the difficulties of applying traditional data warehousing techniques to scientific data.
  • To present the DataFoundry project as a solution for scientific data integration.

Main Methods:

  • Discusses schema integration challenges in scientific databases.
  • Highlights the issue of dynamic schema evolution in scientific data sources.
  • Introduces DataFoundry's unique integration strategy and novel architecture.

Main Results:

Related Experiment Videos

  • Identifies schema complexity and dynamic schema changes as key barriers.
  • Proposes an integration strategy that identifies corresponding instances while preserving differences.
  • Details a novel architecture and meta-data infrastructure to lower warehouse maintenance costs.

Conclusions:

  • Traditional data warehousing methods are inadequate for scientific data.
  • DataFoundry's approach offers a viable solution for scientific data integration and warehousing.
  • The project demonstrates reduced costs for maintaining scientific data warehouses.