基于时空标签轨迹的k近邻模式匹配查询

许建秋; 梁珺秀; 秦小麟

doi:10.11959/j.issn.1000-436x.2018063

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基于时空标签轨迹的k近邻模式匹配查询

学术论文 | 更新时间：2024-06-05

- 基于时空标签轨迹的k近邻模式匹配查询
- k nearest neighbor pattern match queries over spatio-temporal label trajectories
- 通信学报 2018年39卷第4期页码：112-122
- 作者机构：
  
  南京航空航天大学计算机科学与技术学院，江苏南京 211106
- 作者简介：
  
  [ "许建秋（1982-），男，江苏南京人，博士，南京航空航天大学副教授，主要研究方向为移动对象数据库。" ]
  [ "梁珺秀（1994-），女，江西抚州人，南京航空航天大学硕士生，主要研究方向为移动对象数据库。" ]
  [ "秦小麟（1953-），男，江苏南京人，博士，南京航空航天大学教授，主要研究方向为安全数据库、时空数据库、分布式环境数据管理与安全等。" ]
- 基金信息：
  
  中央高校基本科研业务费专项基金资助项目(NS2017073)
- DOI：10.11959/j.issn.1000-436x.2018063
  中图分类号： TP311
- 网络出版日期：2018-04，
  
  纸质出版日期：2018-04-25
- 稿件说明：
移动端阅览
许建秋, 梁珺秀, 秦小麟. 基于时空标签轨迹的k近邻模式匹配查询[J]. 通信学报, 2018,39(4):112-122.

Jianqiu XU, Junxiu LIANG, Xiaolin QIN. k nearest neighbor pattern match queries over spatio-temporal label trajectories[J]. Journal on communications, 2018, 39(4): 112-122.
许建秋, 梁珺秀, 秦小麟. 基于时空标签轨迹的k近邻模式匹配查询[J]. 通信学报, 2018,39(4):112-122. DOI： 10.11959/j.issn.1000-436x.2018063.

Jianqiu XU, Junxiu LIANG, Xiaolin QIN. k nearest neighbor pattern match queries over spatio-temporal label trajectories[J]. Journal on communications, 2018, 39(4): 112-122. DOI： 10.11959/j.issn.1000-436x.2018063.

摘要

时空标签轨迹在传统的时空轨迹数据基础之上融入了具有语义含义的标签信息，丰富了移动对象数据。针对该数据提出k近邻模式匹配查询，即在给定时间区间内匹配相应的模式且距离查询轨迹最近的k条轨迹。设计并实现标签R树（LR-Tree），即增加标签表并在R树每项中添加标签位图，及基于LR-Tree的k近邻模式匹配查询算法。通过真实数据和合成数据将LR-Tree与3DR-Tree、SETI及TB-Tree进行对比，实验表明LR-Tree具有更好的剪枝能力，从而验证了所提算法及索引的有效性。

Abstract

Spatio-temporal label trajectories extended traditional spatio-temporal trajectories with semantic labels.k nearest neighbor pattern match was proposed to return the k nearest trajectories that fulfilled the temporal pattern condition.The Label R-Tree (LR-Tree for short) was proposed

which appending a label table and adding label bitmap in each entry

and k nearest neighbor pattern match query algorithm based on LR-Tree was designed.Using both real and synthetic datasets

the LR-Tree was extensively evaluated in comparison with 3DR-Tree

SETI and TB-Tree.The experimental results demonstrate that LR-Tree showing better pruning ability

and verify the effectiveness of proposed algorithm and index.

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references

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