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SHIFT-SPLIT: I/O efficient maintenance of wavelet-transformed multidimensional data

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dc.contributor.author Jahangiri, M en
dc.contributor.author Sacharidis, D en
dc.contributor.author Shahabi, C en
dc.date.accessioned 2014-03-01T02:43:32Z
dc.date.available 2014-03-01T02:43:32Z
dc.date.issued 2005 en
dc.identifier.issn 07308078 en
dc.identifier.uri http://hdl.handle.net/123456789/31460
dc.subject Cost Reduction en
dc.subject Data Reconstruction en
dc.subject Data Stream en
dc.subject Discrete Wavelet Transform en
dc.subject massive datasets en
dc.subject Multidimensional Data en
dc.subject Wavelet Transform en
dc.subject.other Data maintenance en
dc.subject.other Discrete Wavelet Transform en
dc.subject.other Partial data reconstruction en
dc.subject.other Wavelet trees en
dc.subject.other Approximation theory en
dc.subject.other Data structures en
dc.subject.other Database systems en
dc.subject.other Discrete time control systems en
dc.subject.other Input output programs en
dc.subject.other Trees (mathematics) en
dc.subject.other Wavelet transforms en
dc.title SHIFT-SPLIT: I/O efficient maintenance of wavelet-transformed multidimensional data en
heal.type conferenceItem en
heal.identifier.primary 10.1145/1066157.1066189 en
heal.identifier.secondary http://dx.doi.org/10.1145/1066157.1066189 en
heal.publicationDate 2005 en
heal.abstract The Discrete Wavelet Transform is a proven tool for a wide range of database applications. However, despite broad acceptance, some: of its properties have not been fully explored and thus not exploited, particularly for two common forms of multidimensional decomposition. We^ introduce two-novel operations for wavelet transformed data, termed SHIFT and SPLIT, based on the properties of wavelet trees, which work directly in the wavelet domain. We demonstrate their significance and usefulness by analytically proving six important results in four common data maintenance scenarios, i.e., transformation of massive datasets, appending data, approximation of data streams and partial data reconstruction, leading to significant I/O cost reduction in all cases. Furthermore, we show how these operations can be further improved in combination with the optimal coefficient-to-disk-block allocation strategy. Our exhaustive set of empirical experiments with real-world datasets verifies our claims. Copyright 2005 ACM. en
heal.journalName Proceedings of the ACM SIGMOD International Conference on Management of Data en
dc.identifier.doi 10.1145/1066157.1066189 en
dc.identifier.spage 275 en
dc.identifier.epage 286 en


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