Computational geometry in C (2nd ed.)
Computational geometry in C (2nd ed.)
Updating and Querying Databases that Track Mobile Units
Distributed and Parallel Databases - Special issue on mobile data management and applications
Querying the trajectories of on-line mobile objects
Proceedings of the 2nd ACM international workshop on Data engineering for wireless and mobile access
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
Capturing the Uncertainty of Moving-Object Representations
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Querying Imprecise Data in Moving Object Environments
IEEE Transactions on Knowledge and Data Engineering
Curve-Based Representation of Moving Object Trajectories
IDEAS '04 Proceedings of the International Database Engineering and Applications Symposium
A spatiotemporal uncertainty model of degree 1.5 for continuously changing data objects
Proceedings of the 2006 ACM symposium on Applied computing
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
MBR models for uncertainty regions of moving objects
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
On Managing Very Large Sensor-Network Data Using Bigtable
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
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To support emerging database applications that deal with continuously changing (or moving) data objects (CCDOs), such as vehicles, RFIDs, and multi-stimuli sensors, one requires an efficient data management system that can store, update, and retrieve large sets of CCDOs. Although actual CCDOs can continuously change over time, computer systems cannot deal with continuously occurring infinitesimal changes. Thus, in the data management system, each object's spatiotemporal values are associated with a certain degree of uncertainty at virtually every point in time, and the queries are mostly processed over estimates characterizing the uncertainty. The smaller the uncertainty is, the better the query performance becomes. The paper proposes a sophisticated asymmetric uncertainty model, called the Tornado Model, which can effectively represent, process, and minimize the data uncertainty for a wide variety of CCDO database applications.