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Efficient Evaluation of Partial Match Queries for XML Documents Using Information Retrieval Techniques

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dc.contributor.author박영호-
dc.contributor.authorKyu-Young Whang-
dc.contributor.authorByung Suk Lee-
dc.contributor.authorWook-Shin Han-
dc.date.accessioned2022-04-19T11:43:19Z-
dc.date.available2022-04-19T11:43:19Z-
dc.date.issued2005-04-
dc.identifier.issn0302-9743-
dc.identifier.issn1611-3349-
dc.identifier.urihttps://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/148791-
dc.description.abstractWe propose XIR, a novel method for processing partial match queries on heterogeneous XML documents using information retrieval (IR) techniques. A partial match query is defined as the one having the descendent-or-self axis "//" in its path expression. In its general form, a partial match query has branch predicates forming branching paths. The objective of XIR is to efficiently support this type of queries for large-scale documents of heterogeneous schemas. XIR has its basis on the conventional schema-level methods using relational tables and significantly improves their efficiency using two techniques: an inverted index technique and a novel prefix match join. The former indexes the labels in label paths as keywords in texts, and allows for finding the label paths matching the queries more efficiently than string match used in the conventional methods. The latter supports branching path expressions, and allows for finding the result nodes more efficiently than containment joins used in the conventional methods. We compare the efficiency of XIR with those of XRel and XParent using XML documents crawled from the Internet. The results show that XIR is more efficient than both XRel and XParent by several orders of magnitude for linear path expressions, and by several factors for branching path expressions.-
dc.format.extent18-
dc.language영어-
dc.language.isoENG-
dc.publisherSpringer Verlag-
dc.titleEfficient Evaluation of Partial Match Queries for XML Documents Using Information Retrieval Techniques-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1007/11408079_11-
dc.identifier.scopusid2-s2.0-24644434504-
dc.identifier.wosid000229213600008-
dc.identifier.bibliographicCitationLecture Notes in Computer Science, v.3453, pp 95 - 112-
dc.citation.titleLecture Notes in Computer Science-
dc.citation.volume3453-
dc.citation.startPage95-
dc.citation.endPage112-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryComputer Science, Theory & Methods-
dc.subject.keywordAuthorQuery Processing-
dc.subject.keywordAuthorInverted Index-
dc.subject.keywordAuthorQuery Pattern-
dc.subject.keywordAuthorPath Expression-
dc.subject.keywordAuthorVery Large Data Base-
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공과대학 (인공지능공학부)
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