Efficient Inverted Lists and Query Algorithms for Structured Value Ranking in Update-Intensive Relational Databases
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Abstract
We 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.
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2004-07-06
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Cornell University
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computer science; technical report
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http://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cis/TR2004-1943
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technical report