Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/35908
Title: Managing evolving uncertainty in trajectory databases
Authors: Jeung, HY
Lu, H
Sathe, S
Yiu, ML 
Keywords: Query processing
Spatial databases and GIS
Probabilistic algorithms
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on knowledge and data engineering, 2014, v. 26, no. 7, p. 1692-1705 How to cite?
Journal: IEEE transactions on knowledge and data engineering 
Abstract: Modern positioning technologies enable collecting trajectories from moving objects across different locations over time, typically containing time-varying measurement errors of positioning systems. Unfortunately, current models on uncertain trajectories are incapable of capturing dynamically changing uncertainty in trajectory data, and lack the support of recent progress made in improving localization accuracy. In order to tackle these problems, we address three important issues centric to uncertain trajectory management. First, we propose a flexible trajectory modeling approach that takes into account model-inferred actual positions, time-varying uncertainty, and nondeterministic uncertainty ranges. Second, we develop three estimators that effectively infer evolving densities of trajectory data. Last, we present an efficient mechanism to evaluate probabilistic range queries on those evolving-density trajectories. Empirical results on two large-scale real datasets demonstrate the quality and efficiency of our approach.
URI: http://hdl.handle.net/10397/35908
ISSN: 1041-4347
DOI: 10.1109/TKDE.2013.141
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