Issue No. 07 - July (2014 vol. 26)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2013.141
Hoyoung Jeung , , SAP Research, Brisbane, QLD, Australia
Hua Lu , Department of Computer Science, Aalborg University, Aalborg East, Denmark
Saket Sathe , , EPFL, Lausanne, Switzerland
Man Lung Yiu , Department of Computing, Hong Kong Polytechnic University, Hong Kong
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.
visual databases, geographic information systems, probability, query processing
H. Jeung, H. Lu, S. Sathe and M. L. Yiu, "Managing Evolving Uncertainty in Trajectory Databases," in IEEE Transactions on Knowledge & Data Engineering, vol. 26, no. 7, pp. 1692-1705, 2014.