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Race: finding and ranking compact connected trees for keyword proximity search over xml documents

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Race: finding and ranking compact connected trees for keyword proximity search over xml documents
In this paper, we study the problem of keyword proximity search over XML documents and leverage the efficiency and effectiveness. We take the disjunctive semantics among input keywords into consideration and identify meaningful compact connected trees as the answers of keyword proximity queries. We introduce the notions of Compact Lowest Common Ancestor (CLCA) and Maximal CLCA (MCLCA) and propose Compact Connected Trees (CCTrees) and Maximal CCTrees (MCCTrees) to efficiently and effectively answer keyword queries. We propose a novel ranking mechanism, RACE, to Rank compAct Connected trEes, by taking into consideration both the structural similarity and the textual similarity. Our extensive experimental study shows that our method achieves both high search efficiency and effectiveness, and outperforms existing approaches significantly. Categories and Subject Descriptors H.2.8 [Database Applications ]: Miscellaneous General Terms Algorithms, Performance, Languages Keywords Lowest Common...
Guoliang Li, Jianhua Feng, Jianyong Wang, Bei Yu,
Added 21 Nov 2009
Updated 21 Nov 2009
Type Conference
Year 2008
Where WWW
Authors Guoliang Li, Jianhua Feng, Jianyong Wang, Bei Yu, Yukai He
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