随着时空数据库技术和无线移动通信技术的不断发展，对空间对象时域信息的研究和应用显得日益重要，目前移动对象数据库技术广泛应用在城市规划、交通管理、物流配送、移动电子商务、天气预报等场景。针对移动对象的动态特性和不确定性等特点所造成的技术局限性，项目重点研究结合情境语义分析的高效查询、基于轨迹结构和高斯混合模型的预测方法以及融合移动模式检测与情境感知的可视化技术等关键问题。结合地理空间信息，通过本体匹配和关键词推荐等情境语义分析技术，消除关键词歧义，缩小范围，提高查询效率并关注隐私保护；提取轨迹结构特征，利用半监督学习技术，构建基于高斯混合模型轨迹预测算法，确保适应性和鲁棒性；基于时空动态情境和轮廓的自动构建技术，使用模式匹配和本体论层次结构，尝试解决如何在虚拟世界中高效可视化真实世界中的动态信息。最后实现原型系统并以厦门市车辆智能管理与服务平台为基础开展深入应用研究，学术价值较高，易于扩展。

With the continuous development of spatial-temporal database technology and wireless mobile communication technology, the research and application of temporal information of spatial objects become increasingly important. Currently, the moving objects database technology has been applied to urban planning, traffic management, physical distribution, mobile e-commerce, weather forecast, etc. Facing the limitations of existing technologies caused by the characteristics of moving objects (such as dynamics, uncertainty), this project investigates into the key problems in moving objects database. The main contents are: efficient query utilizing context-aware semantic analysis, trajectory prediction based on trajectory structures and Gaussian Mixture Models (GMM), and visualization technology integrating moving-mode detection and context-awareness. Firstly, for efficient query, we will use the ontology matching and keywords recommendation algorithms to combine geospatial information for keyword disambiguation and providing a narrower scope to the search, which will improve the query efficiency. Meanwhile, privacy-preserving processing will be considered in query as well. Secondly, to achieve adaptive and robust trajectory prediction, we will extract structural features of trajectories and build prediction algorithms based on GMM. Based on these, the semi-supervised learning framework is adopted for trajectory analysis and prediction. Thirdly, for visualizing the dynamic information of the real world in the virtual world, we will use pattern matching and ontology hierarchies to efficiently visualize the information based on automatic construction of spatial-temporal dynamic context and profiles. Finally, we will realize the prototype system based on the above research, and conduct the research in the applications based on the Platform of Intelligent Vehicle Management and Service in Xiamen city. The project has high academic values and is easy to extend to related areas and various applications.