User Motion Prediction in Large Virtual Environments

Abstract

Motion prediction of various objects is important for work of many people. We have to distinguish between near and distant time prediction queries. The trajectory of an object represented by mathematical functions can be used for near time prediction. These formulas are often called motion functions and they use recent movements to predict future locations of the objects. It is impossible to use simple mathematical formulas to evaluate distant time queries, because the movement trajectory between current and distant future time can alter widely. Trajectory pattern of an object is suitable prediction method to take into account for both near and distant time queries. Consequently, data mining methods can mine trajectory patterns from historic movements and these patterns can be used to predict the future objects movement. The best contemporary methods exploit combination of trajectory pattern method and motion function. This means that in case no trajectory pattern is found, the motion function is used to determine object near location. Using the trajectory pattern prediction principle a new approach to optimize communication between client and server in large virtual environments is introduced. Both short time and long time prediction queries are used to minimize the overall amount of downloaded data from network server and to obtain the probably requested parts of the scene in advance.