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dc.contributor.authorAn, Jiyuan
dc.contributor.authorChen, Yi-Ping Phoebe
dc.contributor.authorChen, Hanxiong
dc.date.accessioned2017-05-03T16:57:58Z
dc.date.available2017-05-03T16:57:58Z
dc.date.issued2005
dc.date.modified2009-11-26T09:45:45Z
dc.identifier.issn03064379
dc.identifier.doi10.1016/j.is.2004.05.001
dc.identifier.urihttp://hdl.handle.net/10072/26922
dc.description.abstractThe tree index structure is a traditional method for searching similar data in large datasets. It is based on the presupposition that most sub-trees are pruned in the searching process. As a result, the number of page accesses is reduced. However, time-series datasets generally have a very high dimensionality. Because of the so-called dimensionality curse, the pruning effectiveness is reduced in high dimensionality. Consequently, the tree index structure is not a suitable method for time-series datasets. In this paper, we propose a two-phase (filtering and refinement) method for searching time-series datasets. In the filtering step, a quantizing time-series is used to construct a compact file which is scanned for filtering out irrelevant. A small set of candidates is translated to the second step for refinement. In this step, we introduce an effective index compression method named grid-based datawise dimensionality reduction (DRR) which attempts to preserve the characteristics of the time-series. An experimental comparison with existing techniques demonstrates the utility of our approach.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.publisher.placeUnited Kingdom
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom333
dc.relation.ispartofpageto348
dc.relation.ispartofissue5
dc.relation.ispartofjournalInformation Systems
dc.relation.ispartofvolume30
dc.rights.retentionY
dc.subject.fieldofresearchData Structures
dc.subject.fieldofresearchInformation Systems
dc.subject.fieldofresearchcode080403
dc.subject.fieldofresearchcode0806
dc.titleDDR: an index method for large time-series datasets
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.date.issued2005
gro.hasfulltextNo Full Text
gro.griffith.authorAn, Jay


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