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Article: Indexing continuously changing data with mean-variance tree

TitleIndexing continuously changing data with mean-variance tree
Authors
KeywordsData streaming
Indexing
Query and update processing
Issue Date2008
PublisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijhpcn
Citation
International Journal Of High Performance Computing And Networking, 2008, v. 5 n. 4, p. 263-272 How to Cite?
AbstractTraditional spatial indexes like R-tree usually assume the database is not updated frequently. In applications like location-based services and sensor networks, this assumption is no longer true since data updates can be numerous and frequent. As a result these indexes can suffer from a high update overhead, leading to poor performance. In this paper we propose a novel index structure, the Mean Variance Tree (MVTree), which is built based on the mean and variance of the data instead of the actual data values that can change continuously. Since the mean and variance are relatively stable features compared to the actual values, the MVTree significantly reduces the index update cost. The mean and the variance of the data item can be dynamically adjusted to match the observed fluctuation of the data. Our experiments show that the MVTree substantially improves index update performance while maintaining satisfactory query performance. Copyright © 2008, Inderscience Publishers.
Persistent Identifierhttp://hdl.handle.net/10722/132210
ISSN
2015 SCImago Journal Rankings: 0.362
References

 

DC FieldValueLanguage
dc.contributor.authorXia, Yen_HK
dc.contributor.authorCheng, Ren_HK
dc.contributor.authorPrabhakar, Sen_HK
dc.contributor.authorLei, Sen_HK
dc.contributor.authorShah, Ren_HK
dc.date.accessioned2011-03-21T09:01:57Z-
dc.date.available2011-03-21T09:01:57Z-
dc.date.issued2008en_HK
dc.identifier.citationInternational Journal Of High Performance Computing And Networking, 2008, v. 5 n. 4, p. 263-272en_HK
dc.identifier.issn1740-0562en_HK
dc.identifier.urihttp://hdl.handle.net/10722/132210-
dc.description.abstractTraditional spatial indexes like R-tree usually assume the database is not updated frequently. In applications like location-based services and sensor networks, this assumption is no longer true since data updates can be numerous and frequent. As a result these indexes can suffer from a high update overhead, leading to poor performance. In this paper we propose a novel index structure, the Mean Variance Tree (MVTree), which is built based on the mean and variance of the data instead of the actual data values that can change continuously. Since the mean and variance are relatively stable features compared to the actual values, the MVTree significantly reduces the index update cost. The mean and the variance of the data item can be dynamically adjusted to match the observed fluctuation of the data. Our experiments show that the MVTree substantially improves index update performance while maintaining satisfactory query performance. Copyright © 2008, Inderscience Publishers.en_HK
dc.languageengen_US
dc.publisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijhpcnen_HK
dc.relation.ispartofInternational Journal of High Performance Computing and Networkingen_HK
dc.rightsInternational Journal of High Performance Computing and Networking. Copyright © Inderscience Publishers.en_US
dc.subjectData streamingen_HK
dc.subjectIndexingen_HK
dc.subjectQuery and update processingen_HK
dc.titleIndexing continuously changing data with mean-variance treeen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1740-0562&volume=5&issue=4&spage=263&epage=272&date=2008&atitle=Indexing+continuously+changing+data+with+mean-variance+tree-
dc.identifier.emailCheng, R:ckcheng@cs.hku.hken_HK
dc.identifier.authorityCheng, R=rp00074en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1504/IJHPCN.2008.022302en_HK
dc.identifier.scopuseid_2-s2.0-58149302521en_HK
dc.identifier.hkuros176447en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-58149302521&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume5en_HK
dc.identifier.issue4en_HK
dc.identifier.spage263en_HK
dc.identifier.epage272en_HK
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridXia, Y=8557162400en_HK
dc.identifier.scopusauthoridCheng, R=7201955416en_HK
dc.identifier.scopusauthoridPrabhakar, S=7101672592en_HK
dc.identifier.scopusauthoridLei, S=8557162300en_HK
dc.identifier.scopusauthoridShah, R=35365088300en_HK

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