Topological relations in the world of minimum bounding rectangles: a study with R-trees
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Updating and Querying Databases that Track Mobile Units
Distributed and Parallel Databases - Special issue on mobile data management and applications
Modeling Moving Objects over Multiple Granularities
Annals of Mathematics and Artificial Intelligence
The Geometry of Uncertainty in Moving Objects Databases
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
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
PA-tree: a parametric indexing scheme for spatio-temporal trajectories
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
The Truncated Tornado in TMBB: A Spatiotemporal Uncertainty Model for Moving Objects
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
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The increase in the advanced location based services such as traffic coordination and management necessitates the need for advanced models tracking the positions of Moving Objects (MOs) like vehicles. Computers cannot continuously update locations of MOs because of computational overhead, which limits the accuracy of evaluating MOs' positions. Due to the uncertain nature of such positions, efficiently managing and quantifying the uncertainty regions of MOs are needed in order to improve query response time. These regions can be rather irregular which makes them unsuitable for indexing. This paper presents Minimum Bounding Rectangles (MBR) approximations for three uncertainty region models, namely, the Cylinder Model (CM), the Funnel Model of Degree 1 (FMD1) and the Funnel Model of Degree 2 (FMD2). We also propose an estimation of the MBR of FMD2 that achieves a good balance between computation time and selectivity (false-hits). Extensive experiments on both synthetic and real spatio-temporal datasets showed an order of magnitude improvement of the estimated model over the other modeling methods in terms of the number of MBRs retrieved during query process, which directly corresponds to the number of physical page accesses.