Probabilistic range monitoring of streaming uncertain positions in GeoSocial networks

Patroumpas, K; Papamichalis, M; Sellis, T

dc.contributor.author	Patroumpas, K	en
dc.contributor.author	Papamichalis, M	en
dc.contributor.author	Sellis, T	en
dc.date.accessioned	2014-03-01T02:54:00Z
dc.date.available	2014-03-01T02:54:00Z
dc.date.issued	2012	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36526
dc.subject.other	Central servers	en
dc.subject.other	Degree of uncertainty	en
dc.subject.other	Experimental studies	en
dc.subject.other	Geographic location	en
dc.subject.other	Multiple user	en
dc.subject.other	Networking services	en
dc.subject.other	Probabilistic properties	en
dc.subject.other	Range query	en
dc.subject.other	Region of interest	en
dc.subject.other	Time varying	en
dc.subject.other	Uncertainty models	en
dc.subject.other	Image segmentation	en
dc.subject.other	Uncertainty analysis	en
dc.title	Probabilistic range monitoring of streaming uncertain positions in GeoSocial networks	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-642-31235-9_2	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-31235-9_2	en
heal.publicationDate	2012	en
heal.abstract	We consider a social networking service where numerous subscribers consent to disclose their current geographic location to a central server, but with a varying degree of uncertainty in order to protect their privacy. We aim to effectively provide instant response to multiple user requests, each focusing at continuously monitoring possible presence of their friends or followers in a time-varying region of interest. Every continuous range query must also specify a cutoff threshold for filtering out results with small appearance likelihood; for instance, a user may wish to identify her friends currently located somewhere in the city center with a probability no less than 75%. Assuming a continuous uncertainty model for streaming positional updates, we develop novel pruning heuristics based on spatial and probabilistic properties of the data so as to avoid examination of non-qualifying candidates. Approximate answers are reported with confidence margins, as a means of providing quality guarantees and suppressing useless messages. We complement our analysis with a comprehensive experimental study, which indicates that the proposed technique offers almost real-time notification with tolerable error for diverse query workloads under fluctuating uncertainty conditions. © 2012 Springer-Verlag.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-642-31235-9_2	en
dc.identifier.volume	7338 LNCS	en
dc.identifier.spage	20	en
dc.identifier.epage	37	en