OBBTree: a hierarchical structure for rapid interference detection
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Modeling Moving Objects over Multiple Granularities
Annals of Mathematics and Artificial Intelligence
Proceedings of the Ninth International Conference on Data Engineering
On the Generation of Spatiotemporal Datasets
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Capturing the Uncertainty of Moving-Object Representations
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Managing uncertainty in moving objects databases
ACM Transactions on Database Systems (TODS)
A spatiotemporal uncertainty model of degree 1.5 for continuously changing data objects
Proceedings of the 2006 ACM symposium on Applied computing
The tornado model: uncertainty model for continuously changing data
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
MBR models for uncertainty regions of moving objects
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
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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 TruncatedTornadomodel as a significant advance in minimizing uncertainty region sizes. The TruncatedTornadomodel 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 TiltedMinimumBoundingBox(TMBB) approximation. Through experimental evaluations we show that TruncatedTornadoin TMBBresults in orders of magnitude reduction in volume compared to a recently proposed model called the Tornadomodel and to the standard "Cone" model when approximated by axis-parallel MBB.