Approximate order-k Voronoi cells over positional streams

Patroumpas, K; Minogiannis, T; Sellis, T

dc.contributor.author	Patroumpas, K	en
dc.contributor.author	Minogiannis, T	en
dc.contributor.author	Sellis, T	en
dc.date.accessioned	2014-03-01T02:44:28Z
dc.date.available	2014-03-01T02:44:28Z
dc.date.issued	2007	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31840
dc.subject	approximation	en
dc.subject	data streams	en
dc.subject	moving objects	en
dc.subject	nearest neighbors	en
dc.subject	Voronoi cell	en
dc.subject.other	approximation	en
dc.subject.other	Data stream	en
dc.subject.other	Moving objects	en
dc.subject.other	Nearest neighbors	en
dc.subject.other	Voronoi cell	en
dc.subject.other	Information systems	en
dc.subject.other	Membership functions	en
dc.subject.other	Query processing	en
dc.subject.other	Text processing	en
dc.subject.other	Geographic information systems	en
dc.title	Approximate order-k Voronoi cells over positional streams	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1145/1341012.1341059	en
heal.identifier.secondary	http://dx.doi.org/10.1145/1341012.1341059	en
heal.publicationDate	2007	en
heal.abstract	Handling streams of positional updates from numerous moving objects has become a challenging task for many monitoring applications. Several algorithms have been recently proposed for providing exact answers particularly to continuous range and k-nearest neighbor queries against current object positions. In this work, we introduce a processing technique for efficiently maintaining an approximate order-k Voronoi cell around a certain point of interest when all objects continuously change their locations. This heuristic can easily provide a fairly reliable estimate of the k-nearest neighbors for any query point found inside the constructed cell. We further extend our method to handle positional updates that are not received concurrently for all objects, but instead remain valid for a specific time interval according to a sliding window model. Extensive experimental analysis over synthetic datasets confirms the robustness and scalability of this approach offering near real-time cell maintenance with acceptable error margins. © 2007 ACM.	en
heal.journalName	GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems	en
dc.identifier.doi	10.1145/1341012.1341059	en
dc.identifier.spage	276	en
dc.identifier.epage	283	en