Abstract
Currently, huge amount of data, resulting from the continuous tracking of moving objects, are collected, and stored in appropriate repositories. From these data trajectory data are generated and analyzed to produce knowledge useful for decision-making. Obviously, trajectory data sets need efficient and effective analysis and mining processes to infer mobility patterns and consequently constitute rich sources for many contributions such those related to predictions. Most of researches, presented in the literature, focus on tracking and predicting moving object positions, without taking into account their ever-changing contexts and their environmental impacts. Any environment surrounding any object is dynamic vs static and its influence goes beyond current state to the predicted ones. The aim of this paper is not only to propose a new approach to predict the future position of a moving object based on mobility patterns but also it takes into account the ever-evolving contexts and environments of the underlying objects. We experimented our approach on real case study datasets related to hurricanes' activities. The proposed approach is performed in three phases. The first phase allows the generation of object mobility patterns. In the second phase, spatiotemporal mobility rules are extracted from the previously generated patterns. In the third and last phase, hurricane future position prediction is accomplished by using the extracted rules enhanced by context and environmental characteristics. The proposed model leads to a generic one representing facts and discovering knowledge through various applications including different mobile objects and their associated patterns, environmental and contexts.