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Index clustering for high-performance sequential index access

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dc.contributor.authorCha, GH-
dc.date.available2021-02-22T16:16:43Z-
dc.date.issued2004-03-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/16126-
dc.description.abstractThis paper presents an index clustering technique called the segment-page clustering (SP-clustering). Most relevant index pages are widely scattered on a disk due to dynamic page allocation, and thus many random disk accesses are required during the query processing. The SP-clustering avoids the scattering by storing the relevant nodes contiguously in a segment that contains a sequence of contiguous disk pages and improves the query performance by offering sequential disk access within a segment. A new cost model is also introduced to estimate the performance of the SP-clustering. It takes account of the physical adjacency of pages read as well as the number of pages accessed. Experimental results demonstrate that the SP-clustering improves the query performance up to several times compared with the traditional ones with respect to the total elapsed time.-
dc.format.extent13-
dc.language영어-
dc.language.isoENG-
dc.publisherSPRINGER-VERLAG BERLIN-
dc.titleIndex clustering for high-performance sequential index access-
dc.typeArticle-
dc.publisher.location독일-
dc.identifier.scopusid2-s2.0-35048851251-
dc.identifier.wosid000189446900003-
dc.identifier.bibliographicCitationDATABASE SYSTEMS FOR ADVANCED APPLICATIONS, v.2973, pp 39 - 51-
dc.citation.titleDATABASE SYSTEMS FOR ADVANCED APPLICATIONS-
dc.citation.volume2973-
dc.citation.startPage39-
dc.citation.endPage51-
dc.type.docTypeArticle; Proceedings Paper-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryComputer Science, Theory & Methods-
dc.subject.keywordPlusTREE-
dc.identifier.urlhttps://link.springer.com/chapter/10.1007/978-3-540-24571-1_3-
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