Publication:
Effective and Efficient Automatic Database Selection

dc.contributor.authorFrench, James
dc.contributor.authorCallan, Jamie
dc.contributor.authorPowell, Allison
dc.date.accessioned2026-01-22T21:47:57Z
dc.date.issued1999-01-01
dc.descriptionOriginal submission date: 2012-10-29T21:22:09Z
dc.description.abstractWe examine a class of database selection algorithms that require only document frequency information. The CORI algorithm is an instance of this class of algorithms. In previous work, we showed that CORI is more effective than gGlOSS when evaluated against a relevance-based standard. In this paper, we introduce a family of other algorithms in this class and examine components of these algorithms and of the CORI algorithm to begin identifying the factors responsible for their performance. We establish that the class of algorithms studied here is more effective and efficient than gGlOSS and is applicable to a wider variety of operational environments. In particular, this methodology is completely decoupled from the database indexing technology so is as useful in heterogeneous environments as in homogeneous environments.
dc.identifierrv042t04f
dc.identifier.citationFrench, James, Allison Powell, and Jamie Callan. "Effective and Efficient Automatic Database Selection." University of Virginia Dept. of Computer Science Tech Report (1999).
dc.identifier.doi10.18130/V36476
dc.identifier.urihttps://doi.org/10.18130/V36476
dc.identifier.urihttps://libraopen.library.virginia.edu/handle/item/9092
dc.languageEnglish
dc.language.isoen
dc.publisherUniversity of Virginia, Department of Computer Science
dc.rightsAll rights reserved (no additional license for public reuse)
dc.titleEffective and Efficient Automatic Database Selection
dc.typeTechnical Report
dspace.entity.typePublication
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relation.isAuthorOfPublication.latestForDiscovery53ad7296-b702-4595-afb1-cae2e59f0d65

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