Probabilistic range monitoring of streaming uncertain positions in GeoSocial networks

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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 http://hdl.handle.net/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

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