DMCA
The Truncated Tornado in TMBB: A Spatiotemporal Uncertainty Model for Moving Objects
BibTeX
@MISC{Alkobaisi_thetruncated,
author = {Shayma Alkobaisi and Wan D. Bae and Seon Ho Kim and Scott T. Leutenegger},
title = {The Truncated Tornado in TMBB: A Spatiotemporal Uncertainty Model for Moving Objects},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Abstract. The uncertainty management problem is one of the key is-sues 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 re-gion sizes. The Truncated Tornado model removes uncertainty region sub-areas that are unreachable due to the maximum velocity and accel-eration of the MOs. To make indexing of the uncertainty regions more tractable we utilize an approximation technique called T ilted Minimum Bounding Box (TMBB) approximation. Through experimental evalu-ations 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 ap-proximated by axis-parallel MBB. 1
Keyphrases
truncated tornado spatiotemporal uncertainty model truncated tornado model experimental evalu-ations significant advance uncertainty re-gion size tornado model standard cone model uncertainty management problem system performance key is-sues uncertainty region size tmbb result magnitude reduction minimum bounding box axis-parallel mbb maximum velocity query accuracy uncertainty model approximation technique uncertainty region uncertainty region sub-areas
