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dc.contributor.authorGuo, Linen_US
dc.contributor.authorShanmugasundaram, Jayavelen_US
dc.contributor.authorBeyer, Kevinen_US
dc.contributor.authorShekita, Eugeneen_US
dc.description.abstractWe propose a new ranking paradigm for relational databases called Structured Value Ranking (SVR). SVR uses {\em structured data values} to score (rank) the results of keyword search queries over text columns. Our main contribution is a new family of inverted list indices and associated query algorithms that can support SVR efficiently in update-intensive databases, where the structured data values (and hence the scores of documents) change frequently. Our experimental results on real and synthetic data sets using BerkeleyDB show that we can support SVR efficiently in relational databases.en_US
dc.format.extent533742 bytes
dc.publisherCornell Universityen_US
dc.subjectcomputer scienceen_US
dc.subjecttechnical reporten_US
dc.titleEfficient Inverted Lists and Query Algorithms for Structured Value Ranking in Update-Intensive Relational Databasesen_US
dc.typetechnical reporten_US

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