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Sampling trajectory streams with spatiotemporal criteria

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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


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