轨迹大数据:数据处理关键技术研究综述
发布时间：2018-10-31 08:57
【摘要】：大数据时代下,移动互联网发展与移动终端的普及形成了海量移动对象轨迹数据.轨迹数据含有丰富的时空特征信息,通过轨迹数据处理技术,可以挖掘人类活动规律与行为特征、城市车辆移动特征、大气环境变化规律等信息.海量的轨迹数据也潜在性地暴露出移动对象行为特征、兴趣爱好和社会习惯等隐私信息,攻击者可以根据轨迹数据挖掘出移动对象的活动场景、位置等属性信息.另外,量子计算因其强大的存储和计算能力成为大数据挖掘重要的理论研究方向,用量子计算技术处理轨迹大数据,可以使一些复杂的问题得到解决并实现更高的效率.对轨迹大数据中数据处理关键技术进行了综述.首先,介绍轨迹数据概念和特征,并且总结了轨迹数据预处理方法,包括噪声滤波、轨迹压缩等;其次,归纳轨迹索引与查询技术以及轨迹数据挖掘已有的研究成果,包括模式挖掘、轨迹分类等;总结了轨迹数据隐私保护技术基本原理和特点,介绍了轨迹大数据支撑技术,如处理框架、数据可视化;也讨论了轨迹数据处理中应用量子计算的可能方式,并且介绍了目前轨迹数据处理中所使用的核心算法所对应的量子算法实现;最后,对轨迹数据处理面临的挑战与未来研究方向进行了总结与展望.
[Abstract]:Under big data era, the development of mobile Internet and the popularity of mobile terminals have formed massive mobile object trajectory data. Trajectory data contain abundant space-time characteristic information. By using track data processing technology, we can mine the characteristics of human activity and behavior, the characteristics of urban vehicle movement, the law of atmospheric environment change and so on. Huge amounts of trajectory data also potentially expose the behavior characteristics interests and social habits of moving objects and other privacy information. An attacker can mine the moving objects' activity scene location and other attribute information based on the trajectory data. In addition, because of its powerful storage and computing ability, quantum computing has become an important theoretical research direction for big data. The use of quantum computing technology to deal with the trajectory big data can solve some complex problems and achieve higher efficiency. The key techniques of data processing in trajectory big data are reviewed. Firstly, the concept and characteristics of trajectory data are introduced, and the methods of trajectory data preprocessing are summarized, including noise filtering, trajectory compression and so on. Secondly, the research achievements of trajectory index and query technology and track data mining are summarized, including pattern mining, trajectory classification and so on. This paper summarizes the basic principles and characteristics of trajectory data privacy protection technology, and introduces the trajectory big data support technology, such as processing framework, data visualization; The possible ways of applying quantum computation in trajectory data processing are also discussed, and the implementation of quantum algorithms corresponding to the core algorithms used in trajectory data processing is also introduced. Finally, the challenges and future research directions of trajectory data processing are summarized and prospected.
【作者单位】： 电子科技大学信息与软件工程学院;Department
【基金】：国家自然科学基金(61602097,61272527) 四川省科技厅计划(2015JY0178) 四川省科技支撑计划(2016GZ0065,2016GZ0063) 中央高校基本科研业务费(ZYGX2014J051,ZYGX2011J066,ZYGX2015J072) 中国博士后基金(2015M572464)~~
【分类号】：TP311.13
[Abstract]:Under big data era, the development of mobile Internet and the popularity of mobile terminals have formed massive mobile object trajectory data. Trajectory data contain abundant space-time characteristic information. By using track data processing technology, we can mine the characteristics of human activity and behavior, the characteristics of urban vehicle movement, the law of atmospheric environment change and so on. Huge amounts of trajectory data also potentially expose the behavior characteristics interests and social habits of moving objects and other privacy information. An attacker can mine the moving objects' activity scene location and other attribute information based on the trajectory data. In addition, because of its powerful storage and computing ability, quantum computing has become an important theoretical research direction for big data. The use of quantum computing technology to deal with the trajectory big data can solve some complex problems and achieve higher efficiency. The key techniques of data processing in trajectory big data are reviewed. Firstly, the concept and characteristics of trajectory data are introduced, and the methods of trajectory data preprocessing are summarized, including noise filtering, trajectory compression and so on. Secondly, the research achievements of trajectory index and query technology and track data mining are summarized, including pattern mining, trajectory classification and so on. This paper summarizes the basic principles and characteristics of trajectory data privacy protection technology, and introduces the trajectory big data support technology, such as processing framework, data visualization; The possible ways of applying quantum computation in trajectory data processing are also discussed, and the implementation of quantum algorithms corresponding to the core algorithms used in trajectory data processing is also introduced. Finally, the challenges and future research directions of trajectory data processing are summarized and prospected.
【作者单位】： 电子科技大学信息与软件工程学院;Department
【基金】：国家自然科学基金(61602097,61272527) 四川省科技厅计划(2015JY0178) 四川省科技支撑计划(2016GZ0065,2016GZ0063) 中央高校基本科研业务费(ZYGX2014J051,ZYGX2011J066,ZYGX2015J072) 中国博士后基金(2015M572464)~~
【分类号】：TP311.13
【参考文献】
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