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dc.contributor.authorStantic, Belaen_US
dc.contributor.authorSattar, Abdulen_US
dc.contributor.authorTerenziani, Paoloen_US
dc.contributor.editorLarry Kerschberg (Editor-in-Chief), Maria Zemankova (Editor-in-Chief), Zbigniew Ras (Editor-in-Chiefen_US
dc.date.accessioned2017-05-03T11:26:22Z
dc.date.available2017-05-03T11:26:22Z
dc.date.issued2009en_US
dc.date.modified2010-08-23T07:00:19Z
dc.identifier.issn15737675en_US
dc.identifier.doi10.1007/s10844-008-0072-5en_AU
dc.identifier.urihttp://hdl.handle.net/10072/22567
dc.description.abstractMost modern database applications involve a significant amount of time dependent data and a significant portion of this data is now-relative. Now-relative data are a natural and meaningful part of every temporal database as well as being the focus of most queries. Previous studies indicate that the choice of the representation of now significantly influences the efficiency of accessing bitemporal data. In this paper we propose and experimentally evaluate a novel approach to represent now that we termed the POINT approach, in which now-relative facts are represented as points on the transaction-time and/or valid-time line. Furthermore, in the POINT approach we propose a logical query transformation that relies on the above representation and on the geometry features of spatial access methods. Such a logical query transformation enables off-the-shelf spatial indexes to be used. We empirically prove that the POINT approach is efficient on now-relative bitemporal data, outperforming the maximum timestamp approach that has been proven to the best approach to now-relative data in the literature, independently of the indexing methodology (B+- tree vs R*- tree) being used. Specifically, if spatial indexing is used, the POINT approach outperforms the maximum timestamp approach to the extent of factor more than 10, both in number of disk accesses and CPU usage.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent873422 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherSpringer New York LLCen_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom297en_US
dc.relation.ispartofpageto323en_US
dc.relation.ispartofissue3en_US
dc.relation.ispartofjournalJournal of Intelligent Information Systems: integrating artificial intelligence and database technologiesen_US
dc.relation.ispartofvolume32en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchHISTORY AND ARCHAEOLOGYen_US
dc.subject.fieldofresearchcode210000en_US
dc.titleThe POINT approach to represent now in bitemporal databasesen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright 2009 Springer Netherlands. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.comen_AU
gro.date.issued2009
gro.hasfulltextFull Text


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