K2/Kleisli and GUS: Experiments in integrated access to genomic data sources
Susan B. Davidson, Jonathan Crabtree, Brian P. Brunk, Jonathan Schug, Val Tannen, G. Christian Overton, Christian J. Stoeckert
Abstract
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Susan B. Davidson, Jonathan Crabtree, Brian P. Brunk, Jonathan Schug, Val Tannen, G. Christian Overton, Christian J. Stoeckert
Abstract
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The integrated access to heterogeneous data sources is a major challenge for the biomedical community. Several solution strategies have been explored: link-driven federation of databases, view integration, and warehousing. In this paper we report on our experiences with two systems that were developed at the University of Pennsylvania: K2, a view integration implementation, and GUS, a data warehouse. Although the view integration and the warehouse approaches each have advantages, there is no clear “winner.” Therefore, in selecting the best strategy for a particular application, users must consider the data characteristics, the performance guarantees required, and the programming resources available. Our experiences also point to some practical tips on how database updates should be published, and how XML can be used to facilitate the processing of updates in a warehousing environment.
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The integrated access to heterogeneous data sources is a major challenge for the biomedical community. Several solution strategies have been explored: link-driven federation of databases, view integration, and warehousing. In this paper we report on our experiences with two systems that were developed at the University of Pennsylvania: K2, a view integration implementation, and GUS, a data warehouse. Although the view integration and the warehouse approaches each have advantages, there is no clear “winner.” Therefore, in selecting the best strategy for a particular application, users must consider the data characteristics, the performance guarantees required, and the programming resources available. Our experiences also point to some practical tips on how database updates should be published, and how XML can be used to facilitate the processing of updates in a warehousing environment.
Key concepts: Computer science, Data warehouse, XML, Data integration, Database, Point (geometry), Data science, System integration