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

Jahangiri, M; Sacharidis, D; Shahabi, C

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	https://dspace.lib.ntua.gr/xmlui/handle/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