|
|||||
|
|
| 面向不确定移动对象的连续K近邻查询算法* | |
| 引用本文： | 于彦伟,齐建鹏,宋鹏,张永刚. 面向不确定移动对象的连续K近邻查询算法*[J]. 模式识别与人工智能, 2016, 29(11): 1048-1056. DOI: 10.16451/j.cnki.issn1003-6059.201611010 |
| 作者姓名： | 于彦伟 齐建鹏 宋鹏 张永刚 |
| 作者单位： | 1.烟台大学 计算机与控制工程学院 烟台 264005 2.吉林大学 符号计算与知识工程教育部重点实验室 长春 130012 |
| 基金项目： | 国家自然科学基金项目(No.61572419,61403328,61302065)、山东省自然科学基金项目(No.ZR2014FQ016,ZR2013FM011)、山东省重点研发计划项目(No.J2015GSF115009)、吉林大学符号计算与知识工程教育部重点实验室开放基金项目(No.93K172014K13)资助 |
| 摘 要： | 近年来，位置服务等领域急需解决的一个难点问题是不确定移动对象连续K近邻查询.基于此情况，文中提出高效的面向不确定移动对象的连续K近邻查询算法.首先提出2种预测移动对象可能区域算法MaxMin与Rate，利用最近一段时间窗口内的位置采样、速度和方向预测移动对象在查询时刻到未来I区间可能的位置区域.同时使用最小距离与最大距离区间描述移动对象到查询对象的距离.然后采用优化的基于模糊可能度判定的排序方法查找查询对象的K近邻.最后在真实和合成的大规模移动对象数据集上验证文中方法的有效性. |
| 关 键 词： | 移动对象 K近邻查询 可能度判定排序 |
| 收稿时间： | 2016-03-02 |
Continuous K-Nearest Neighbor Queries for Uncertain Moving Objects |
|
| YU Yanwei,QI Jianpeng,SONG Peng,ZHANG Yonggang. Continuous K-Nearest Neighbor Queries for Uncertain Moving Objects[J]. Pattern Recognition and Artificial Intelligence, 2016, 29(11): 1048-1056. DOI: 10.16451/j.cnki.issn1003-6059.201611010 | |
| Authors: | YU Yanwei QI Jianpeng SONG Peng ZHANG Yonggang |
| Affiliation: | 1.School of Computer and Control Engineering, Yantai University, Yantai 264005 2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012 |
| Abstract: | An urgent problem in location-based services is continuous K-nearest neighbor (KNN) queries for uncertain moving objects. An efficient algorithm for continuous K-nearest neighbor queries for uncertain moving objects is proposed. Firstly, two solutions, MaxMin and Rate, are proposed to predict the possible location range of the moving object in the time interval by utilizing the sampling points with velocities in the recent time window. A closed interval of minimum and maximum distances is employed to represent the distance between the query object and the moving object. Secondly, an optimized ranking method based on vague possibility decision is proposed to quickly find KNNs of the query object. Finally, experimental results on real and synthetic large-scale datasets demonstrate the effectiveness of the proposed algorithm. |
| Keywords: | Moving Object K-Nearest Neighbor Query Possibility Decision Ranking |
| 点击此处可从《模式识别与人工智能》浏览原始摘要信息 | |
| 点击此处可从《模式识别与人工智能》下载全文 | |
