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Collection selection for managed distributed document databases [An article from: Information Processing and Management]
Book Details
Author(s)D. D'Souza, J.A. Thom, J. Zobel
PublisherElsevier
ISBN / ASINB000RR19IS
ISBN-13978B000RR19I0
AvailabilityAvailable for download now
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Information Processing and Management, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
In a distributed document database system, a query is processed by passing it to a set of individual collections and collating the responses. For a system with many such collections, it is attractive to first identify a small subset of collections as likely to hold documents of interest before interrogating only this small subset in more detail. A method for choosing collections that has been widely investigated is the use of a selection index, which captures broad information about each collection and its documents. In this paper, we re-evaluate several techniques for collection selection. We have constructed new sets of test data that reflect one way in which distributed collections would be used in practice, in contrast to the more artificial division into collections reported in much previous work. Using these managed collections, collection ranking based on document surrogates is more effective than techniques such as CORI that are based on collection lexicons. Moreover, these experiments demonstrate that conclusions drawn from artificial collections are of questionable validity.
Description:
In a distributed document database system, a query is processed by passing it to a set of individual collections and collating the responses. For a system with many such collections, it is attractive to first identify a small subset of collections as likely to hold documents of interest before interrogating only this small subset in more detail. A method for choosing collections that has been widely investigated is the use of a selection index, which captures broad information about each collection and its documents. In this paper, we re-evaluate several techniques for collection selection. We have constructed new sets of test data that reflect one way in which distributed collections would be used in practice, in contrast to the more artificial division into collections reported in much previous work. Using these managed collections, collection ranking based on document surrogates is more effective than techniques such as CORI that are based on collection lexicons. Moreover, these experiments demonstrate that conclusions drawn from artificial collections are of questionable validity.
