Sampling trajectory streams with spatiotemporal criteria

Potamias, M; Patroumpas, K; Sellis, T

dc.contributor.author	Potamias, M	en
dc.contributor.author	Patroumpas, K	en
dc.contributor.author	Sellis, T	en
dc.date.accessioned	2014-03-01T02:44:11Z
dc.date.available	2014-03-01T02:44:11Z
dc.date.issued	2006	en
dc.identifier.issn	10993371	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31733
dc.subject	Communication Cost	en
dc.subject	Continuous Query	en
dc.subject	Data Stream	en
dc.subject	Experimental Study	en
dc.subject	High Dimensionality	en
dc.subject	Load Shedding	en
dc.subject	Moving Object	en
dc.subject	Spatial Locality	en
dc.subject	Trajectory Generation	en
dc.subject.other	approximation techniques	en
dc.subject.other	communication costs	en
dc.subject.other	compression schemes	en
dc.subject.other	Continuous queries	en
dc.subject.other	Data evolution	en
dc.subject.other	Database management	en
dc.subject.other	Environmental databases	en
dc.subject.other	Experimental studies	en
dc.subject.other	geospatial applications	en
dc.subject.other	High-dimensional	en
dc.subject.other	Historical perspective	en
dc.subject.other	Informatics	en
dc.subject.other	international conferences	en
dc.subject.other	Load shedding	en
dc.subject.other	Moving objects	en
dc.subject.other	processing complexity	en
dc.subject.other	Spatial locality	en
dc.subject.other	Spatio-temporal data	en
dc.subject.other	Stability and robustness	en
dc.subject.other	Administrative data processing	en
dc.subject.other	Biodiversity	en
dc.subject.other	Canning	en
dc.subject.other	Data processing	en
dc.subject.other	Industrial research	en
dc.subject.other	Location	en
dc.subject.other	Management information systems	en
dc.subject.other	Pattern matching	en
dc.subject.other	Rivers	en
dc.subject.other	Statistical methods	en
dc.subject.other	Statistics	en
dc.subject.other	Trajectories	en
dc.subject.other	Database systems	en
dc.title	Sampling trajectory streams with spatiotemporal criteria	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/SSDBM.2006.45	en
heal.identifier.secondary	http://dx.doi.org/10.1109/SSDBM.2006.45	en
heal.identifier.secondary	1644324	en
heal.publicationDate	2006	en
heal.abstract	Monitoring movement of high-dimensional points is essential for environmental databases, geospatial applications, and biodiversity informatics as it reveals crucial information about data evolution, provenance detection, pattern matching etc. Despite recent research interest on processing continuous queries in the context of spatiotemporal data streams, the main focus is on managing the current location of numerous moving objects. In this paper, we turn our attention onto a historical perspective of movement and examine trajectories generated by streaming positional updates. The key challenge is how to maintain a concise, yet quite reliable summary of each object's movement, avoiding any superfluous details and saving in processing complexity and communication cost. We propose two single-pass approximation techniques based on sampling that take advantage of the spatial locality and temporal timeliness inherent in trajectory streams. As a means of reducing substantially the scale of the dataseis, we utilize heuristic prediction to distinguish which locations to preserve in the compressed trajectories. A comprehensive experimental study verifies the stability and robustness of the proposed techniques and demonstrates that intelligent compression schemes are able to act as effective load shedding operators achieving remarkable results. © 2006 IEEE.	en
heal.journalName	Proceedings of the International Conference on Scientific and Statistical Database Management, SSDBM	en
dc.identifier.doi	10.1109/SSDBM.2006.45	en
dc.identifier.spage	275	en
dc.identifier.epage	284	en