Window update patterns in stream operators

Patroumpas, K; Sellis, T

dc.contributor.author	Patroumpas, K	en
dc.contributor.author	Sellis, T	en
dc.date.accessioned	2014-03-01T02:46:35Z
dc.date.available	2014-03-01T02:46:35Z
dc.date.issued	2009	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32729
dc.subject	Continuous Query	en
dc.subject	Data Stream	en
dc.subject	Design and Implementation	en
dc.subject	Sliding Window	en
dc.subject	Stream Processing	en
dc.subject.other	Arrival rates	en
dc.subject.other	Continuous queries	en
dc.subject.other	Data sets	en
dc.subject.other	Data stream	en
dc.subject.other	Incremental evaluation	en
dc.subject.other	Sliding Window	en
dc.subject.other	Stream processing	en
dc.subject.other	Update patterns	en
dc.subject.other	Window Size	en
dc.subject.other	Information systems	en
dc.subject.other	Machine design	en
dc.subject.other	Windows	en
dc.title	Window update patterns in stream operators	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-642-03973-7_10	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-03973-7_10	en
heal.publicationDate	2009	en
heal.abstract	Continuous queries applied over nonterminating data streams usually specify windows in order to obtain an evolving -yet restricted- set of tuples and thus provide timely results. Among other typical variants, sliding windows are mostly employed in stream processing engines and several advanced techniques have been suggested for their incremental evaluation. In this paper, we set out to study the existence of monotonic-related semantics in windowing constructs towards a more efficient maintenance of their changing contents. We investigate update patterns observed in common window variants as well as their impact on windowed adaptations of typical operators (like selection, join or aggregation), offering more insight towards design and implementation of stream processing mechanisms. Finally, to demonstrate its significance, this framework is validated for several windowed operations against streaming datasets with simulations at diverse arrival rates and window sizes. © 2009 Springer.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-642-03973-7_10	en
dc.identifier.volume	5739 LNCS	en
dc.identifier.spage	118	en
dc.identifier.epage	132	en