Abstract-Existing prediction methods in moving objects databases cannot forecast locations accurately if the query time is far away from the current time. Even for near future prediction, most techniques assume the trajectory of an object's movements can be represented by some mathematical formulas of motion functions based on its recent movements. However, an object's movements are more complicated than what the mathematical formulas can represent. Prediction based on an object's trajectory patterns is a powerful way and has been investigated by several work. But their main interest is how to discover the patterns. In this paper, we present a novel prediction approach, namely The Hybrid Prediction Model, which estimates an object's future locations based on its pattern information as well as existing motion functions using the object's recent movements. Specifically, an object's trajectory patterns which have ad-hoc forms for prediction are discovered and then indexed by a novel access method for efficient query processing. In addition, two query processing techniques that can provide accurate results for both near and distant time predictive queries are presented. Our extensive experiments demonustrate that proposed techniques are more accurate and efficient than existing forecasting schemes.
Jeung et al., proposed a prediction approach named Hybrid Prediction Model (HPM) REF for mining the trajectory pattern of a moving object.
A Hybrid Prediction Model for Moving Objects
2008 IEEE 24th International Conference on Data Engineering
2008 IEEE 24th International Conference on Data Engineering
Computer Science