Hierarchically compressed wavelet synopses

Sacharidis, D; Deligiannakis, A; Sellis, T

dc.contributor.author	Sacharidis, D	en
dc.contributor.author	Deligiannakis, A	en
dc.contributor.author	Sellis, T	en
dc.date.accessioned	2014-03-01T01:30:50Z
dc.date.available	2014-03-01T01:30:50Z
dc.date.issued	2009	en
dc.identifier.issn	1066-8888	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19644
dc.subject	Compression	en
dc.subject	Data streams	en
dc.subject	Wavelet synopsis	en
dc.subject.classification	Computer Science, Hardware & Architecture	en
dc.subject.classification	Computer Science, Information Systems	en
dc.subject.other	Coefficient values	en
dc.subject.other	Compression	en
dc.subject.other	Compression schemes	en
dc.subject.other	Construction algorithms	en
dc.subject.other	Data sets	en
dc.subject.other	Data streams	en
dc.subject.other	Data values	en
dc.subject.other	Error metric	en
dc.subject.other	Greedy algorithms	en
dc.subject.other	Hierarchical relationships	en
dc.subject.other	Large data sets	en
dc.subject.other	Optimal sets	en
dc.subject.other	Real data sets	en
dc.subject.other	Research studies	en
dc.subject.other	Space constraints	en
dc.subject.other	Sum squared errors	en
dc.subject.other	Tree structures	en
dc.subject.other	Wavelet coefficients	en
dc.subject.other	Wavelet synopsis	en
dc.subject.other	Data mining	en
dc.subject.other	Trees (mathematics)	en
dc.subject.other	Wavelet decomposition	en
dc.subject.other	Wavelet transforms	en
dc.subject.other	Wireless telecommunication systems	en
dc.subject.other	Data compression	en
dc.title	Hierarchically compressed wavelet synopses	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s00778-008-0096-z	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s00778-008-0096-z	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	The wavelet decomposition is a proven tool for constructing concise synopses of large data sets that can be used to obtain fast approximate answers. Existing research studies focus on selecting an optimal set of wavelet coefficients to store so as to minimize some error metric, without however seeking to reduce the size of the wavelet coefficients themselves. In many real data sets the existence of large spikes in the data values results in many large coefficient values lying on paths of a conceptual tree structure known as the error tree. To exploit this fact, we introduce in this paper a novel compression scheme for wavelet synopses, termed hierarchically compressed wavelet synopses, that fully exploits hierarchical relationships among coefficients in order to reduce their storage. Our proposed compression scheme allows for a larger number of coefficients to be stored for a given space constraint thus resulting in increased accuracy of the produced synopsis. We propose optimal, approximate and greedy algorithms for constructing hierarchically compressed wavelet synopses that minimize the sum squared error while not exceeding a given space budget. Extensive experimental results on both synthetic and real-world data sets validate our novel compression scheme and demonstrate the effectiveness of our algorithms against existing synopsis construction algorithms. © 2008 Springer-Verlag.	en
heal.publisher	SPRINGER	en
heal.journalName	VLDB Journal	en
dc.identifier.doi	10.1007/s00778-008-0096-z	en
dc.identifier.isi	ISI:000262317000009	en
dc.identifier.volume	18	en
dc.identifier.issue	1	en
dc.identifier.spage	203	en
dc.identifier.epage	231	en