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清华大学学报(自然科学版)  2019, Vol. 59 Issue (3): 186-193    DOI: 10.16511/j.cnki.qhdxxb.2018.26.047
  计算机科学与技术 本期目录 | 过刊浏览 | 高级检索 |
面向室内空间的语义轨迹提取框架
骆歆远1, 陈欣1, 寿黎但1,2, 陈珂1,3, 吴妍静4
1. 浙江大学 计算机科学与技术学院, 杭州 310027;
2. 浙江大学 CAD&CG国家重点实验室, 杭州 310027;
3. 浙江省大数据智能计算重点实验室, 杭州 310027;
4. 浙江中医药大学附属第三医院, 杭州 310009
Semantic trajectory extraction framework for indoor space
LUO Xinyuan1, CHEN Xin1, SHOU Lidan1,2, CHEN Ke1,3, WU Yanjing4
1. Department of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China;
2. CAD & CG State Key Lab, Zhejiang University, Hangzhou 310027, China;
3. Key Laboratory of Big Data Intelligent Computing of Zhejiang Province, Hangzhou 310027, China;
4. Third Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou 310009, China
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摘要 利用海量位置数据分析用户行为,挖掘用户的潜在价值越来越受到人们的关注。室外环境中已有较成熟的解决方案。针对室内空间中WiFi定位数据的精确度、鲁棒性不足等问题,对面向室内空间的语义轨迹提取方法进行了研究,能在减少错误、压缩原始位置数据的同时,增强轨迹的表达能力,使得更深入的室内时空数据挖掘成为可能。该文基于室内空间建模、数据清洗、事件提取和语义增强4个模块的框架提出了室内语义轨迹计算的方法,在真实数据集和模拟数据集上进行实验,结果表明:该方法能从存在误差和缺失的室内定位数据中,准确有效地挖掘和提取出含有语义信息的轨迹数据,为上层的应用分析所用。
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骆歆远
陈欣
寿黎但
陈珂
吴妍静
关键词 室内定位室内空间模型语义轨迹密度聚类    
Abstract:Using massive location data to analyze user behavior and tap the potential value of customers is getting much attention. Mature solutions have been studied for outdoor space, while the accuracy and robustness of WiFi positioning data are main challenges for indoor space. The method of extracting the semantic trajectory for indoor space is studied. It can reduce error, compress original position data and enhance the expression capability of trajectory for indoor spatio-temporal data mining. Based on four modules including indoor space modeling, data cleansing, event extraction and semantic enhancement, a framework for calculating indoor semantic trajectory is proposed. Experiments are conducted on real and simulated datasets, and the results show that this method can accurately and effectively extract trajectory data containing semantic information from indoor positioning data with incompleteness and errors, which can be used for high-level analytical applications.
Key wordsindoor positioning    indoor space model    semantic trajectory    density clustering
收稿日期: 2018-07-22      出版日期: 2019-03-19
基金资助:国家基础研究计划项目(2015CB352400);国家自然科学基金资助项目(61672455,61472348);浙江省自然科学基金资助项目(LY18F020005)
引用本文:   
骆歆远, 陈欣, 寿黎但, 陈珂, 吴妍静. 面向室内空间的语义轨迹提取框架[J]. 清华大学学报(自然科学版), 2019, 59(3): 186-193.
LUO Xinyuan, CHEN Xin, SHOU Lidan, CHEN Ke, WU Yanjing. Semantic trajectory extraction framework for indoor space. Journal of Tsinghua University(Science and Technology), 2019, 59(3): 186-193.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2018.26.047  或          http://jst.tsinghuajournals.com/CN/Y2019/V59/I3/186
  图1 语义轨迹样例
  图2 整体系统框架
  图3 漂移定位数据的清洗示例
  图4 基于时空密度的事件提取算法
  图5 邻域、 时间阈值与提取事件个数的关系
  表 1 真实定位数据示例
  表2 4种算法下轨迹准确性对比
  图6 在真实和模拟数据集下各算法的准确性指标对比
  图7 (网络版彩图)室内定位数据分析可视化工具
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