Fast approximate wavelet tracking on streams

Cormode, G; Garofalakis, M; Sacharidis, D

dc.contributor.author	Cormode, G	en
dc.contributor.author	Garofalakis, M	en
dc.contributor.author	Sacharidis, D	en
dc.date.accessioned	2014-03-01T02:44:03Z
dc.date.available	2014-03-01T02:44:03Z
dc.date.issued	2006	en
dc.identifier.issn	0302-9743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31640
dc.subject	Data Distribution	en
dc.subject	Data Stream	en
dc.subject	Experimental Analysis	en
dc.subject	Hierarchical Structure	en
dc.subject	High Speed	en
dc.subject	Network Monitoring	en
dc.subject	Multi Dimensional	en
dc.subject	Real Time	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	Croup-Count Sketch (CCS)	en
dc.subject.other	Experimental analysis	en
dc.subject.other	Network monitoring	en
dc.subject.other	Streaming applications	en
dc.subject.other	Algorithms	en
dc.subject.other	Data acquisition	en
dc.subject.other	Hierarchical systems	en
dc.subject.other	Optimization	en
dc.subject.other	Problem solving	en
dc.subject.other	Query languages	en
dc.subject.other	Wavelet transforms	en
dc.subject.other	Approximation theory	en
dc.title	Fast approximate wavelet tracking on streams	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/11687238_4	en
heal.identifier.secondary	http://dx.doi.org/10.1007/11687238_4	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	Recent years have seen growing interest in effective algorithms for summarizing and querying massive, high-speed data streams, Randomized sketch synopses provide accurate approximations for general-purpose summaries of the streaming data distribution (e.g., wavelets). The focus of existing work has typically been on minimizing space requirements of the maintained synopsis -however, to effectively support high-speed data-stream analysis, a crucial practical requirement is to also optimize: (1) the update time for incorporating a streaming data element in the sketch, and (2) the query time for producing an approximate summary (e.g., the top wavelet coefficients) from the sketch. Such time costs must be small enough to cope with rapid stream-arrival rates and the realtime querying requirements of typical streaming applications (e.g., ISP network monitoring). With cheap and plentiful memory, space is often only a secondary concern after query/update time costs. In this paper, we propose the first fast solution to the problem of tracking wavelet representations of one-dimensional and multi-dimensional data streams, based on a novel stream synopsis, the Croup-Count Sketch (CCS). By imposing a hierarchical structure of groups over the data and applying the GCS, our algorithms can quickly recover the most important wavelet coefficients with guaranteed accuracy. A tradeoff between query time and update time is established, by varying the hierarchical structure of groups, allowing the right balance to be found for specific data stream. Experimental analysis confirms this tradeoff, and shows that all our methods significantly outperform previously known methods in terms of both update time and query time, while maintaining a high level of accuracy. © Springer-Verlag Berlin Heidelberg 2006.	en
heal.publisher	SPRINGER-VERLAG BERLIN	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
heal.bookName	LECTURE NOTES IN COMPUTER SCIENCE	en
dc.identifier.doi	10.1007/11687238_4	en
dc.identifier.isi	ISI:000237081600004	en
dc.identifier.volume	3896 LNCS	en
dc.identifier.spage	4	en
dc.identifier.epage	22	en