Managing evolving uncertainty in trajectory databases

Hoyoung Jeung, Hua Lu, Saket Sathe, Man Lung Yiu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

13 Citations (Scopus)


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.
Original languageEnglish
Article number6579615
Pages (from-to)1692-1705
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number7
Publication statusPublished - 1 Jan 2014


  • Probabilistic algorithms
  • Query processing
  • Spatial databases and GIS

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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