Online amnesie summarization of streaming locations

Potamias, M; Patroumpas, K; Seflis, T

dc.contributor.author	Potamias, M	en
dc.contributor.author	Patroumpas, K	en
dc.contributor.author	Seflis, T	en
dc.date.accessioned	2014-03-01T02:44:52Z
dc.date.available	2014-03-01T02:44:52Z
dc.date.issued	2007	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31992
dc.subject	Continuous Query	en
dc.subject	Data Stream	en
dc.subject	Individual Object	en
dc.subject	Moving Object	en
dc.subject	Time Series	en
dc.subject	Tree Structure	en
dc.subject	Near Real Time	en
dc.subject.other	Online maintenance	en
dc.subject.other	Streaming locations	en
dc.subject.other	Time-decaying approximation	en
dc.subject.other	Approximation theory	en
dc.subject.other	Data structures	en
dc.subject.other	Decision trees	en
dc.subject.other	Query processing	en
dc.subject.other	Requirements engineering	en
dc.subject.other	Storage allocation (computer)	en
dc.subject.other	Online systems	en
dc.title	Online amnesie summarization of streaming locations	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-540-73540-3_9	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-540-73540-3_9	en
heal.publicationDate	2007	en
heal.abstract	Massive data streams of positional updates become increasingly difficult to manage under limited memory resources, especially in terms of providing near real-time response to multiple continuous queries. In this paper, we consider online maintenance for spatiotemporal summaries of streaming positions in an aging-aware fashion, by gradually evicting older observations in favor of greater precision for the most recent portions of movement. Although several amnesic functions have been proposed for approximation of time series, we opt for a simple, yet quite efficient scheme that achieves contiguity along all retained stream pieces. To this end, we adapt an amnesic tree structure that effectively meets the requirements of time-decaying approximation while taking advantage of the succession inherent in positional updates. We further exemplify the significance of this scheme in two important cases: the first one refers to trajectory compression of individual objects; the other offers estimated aggregates of moving object locations across time. Both techniques are validated with comprehensive experiments, confirming their suitability in maintaining online concise synopses for moving objects. © Springer-Verlag Berlin Heidelberg 2007.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-540-73540-3_9	en
dc.identifier.volume	4605 LNCS	en
dc.identifier.spage	148	en
dc.identifier.epage	166	en