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
Implementing a qualitative calculus to analyse moving point objects
Expert Systems with Applications: An International Journal
Interpolating and using most likely trajectories in moving-objects databases
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
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In recent years, many emerging database applications deal with continuously moving data objects 驴 each data object moves continuously and frequently reports its current location, moving direction, and speed to the database server. A database server for these applications keeps track of the trajectories of individual moving objects and processes queries referring to the past or future trajectories. Related techniques view a moving object trajectory as a sequence of connected line segments. However, most natural moving objects, such as airplanes, vessels, and vehicles, draw a smooth trajectory with no angles. This paper presents our curve-based trajectory representation models. The presented results show that the curve-based models provide much more accurate trajectories than the line-based models when we have the same amount of data (same number of reported points). In other words, the curve-based models require a smaller amount of data while providing the same accuracy in trajectory representation.