The truncated tornado in TMBB: A spatiotemporal uncertainty model for moving objects

Shayma Alkobaisi, Petr Vojtěchovský, Wan D. Bae, Seon Ho Kim, Scott T. Leutenegger

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

The uncertainty management problem is one of the key issues associated with moving objects (MOs). Minimizing the uncertainty region size can increase both query accuracy and system performance. In this paper, we propose an uncertainty model called the Truncated Tornado model as a significant advance in minimizing uncertainty region sizes. The Truncated Tornado model removes uncertainty region sub-areas that are unreachable due to the maximum velocity and acceleration of the MOs. To make indexing of the uncertainty regions more tractable we utilize an approximation technique called Tilted Minimum Bounding Box (TMBB) approximation. Through experimental evaluations we show that Truncated Tornado in TMBB results in orders of magnitude reduction in volume compared to a recently proposed model called the Tornado model and to the standard "Cone" model when approximated by axis-parallel MBB.

Original languageEnglish
Title of host publicationDatabase and Expert Systems Applications - 19th International Conference, DEXA 2008, Proceedings
Pages33-40
Number of pages8
DOIs
Publication statusPublished - 2008
Event19th International Conference on Database and Expert Systems Applications, DEXA 2008 - Turin, Italy
Duration: Sept 1 2008Sept 5 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5181 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other19th International Conference on Database and Expert Systems Applications, DEXA 2008
Country/TerritoryItaly
CityTurin
Period9/1/089/5/08

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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