ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2015, Vol. 52 ›› Issue (9): 2002-2013.doi: 10.7544/issn1000-1239.2015.20131230

• 软件技术 • 上一篇    下一篇

支持多种查询的室内移动对象索引

贲婷婷,秦小麟,许建秋   

  1. (南京航空航天大学计算机科学与技术学院 南京 210016) (ben_tingting@126.com)
  • 出版日期: 2015-09-01
  • 基金资助: 
    基金项目:国家自然科学基金项目(61373015);国家自然科学基金青年基金项目(61300052,41301407);国家教育部高等学校博士学科点专项科研基金项目(20103218110017);江苏高校优势学科建设工程项目(PAPD);中央高校基本科研业务费专项资金项目(NP2013307)

Index of Indoor Moving Objects for Multiple Queries

Ben Tingting, Qin Xiaolin, Xu Jianqiu   

  1. (College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016)
  • Online: 2015-09-01

摘要: 随着室内定位技术的广泛应用,室内位置服务快速发展.移动对象索引技术作为支撑位置服务的核心技术,大多数都基于室外环境,难以直接应用于室内空间.现有的室内移动对象索引,仅关注对移动对象历史数据的查询,且支持的查询类型单一.为此,提出MQII(multiple queries indoor index)索引结构,对移动对象历史和当前位置信息进行索引,能够同时支持对象位置查询、轨迹查询以及时空范围查询.索引采用对象链表和桶链表结构,实现从对象和时空范围2个方面对移动对象数据的管理;提出针对该索引结构的有效更新、查询算法;实验结果表明,与现有室内移动对象索引相比,索引不仅能够支持历史查询和当前查询,还能够同时高效支持对象位置查询、轨迹查询和范围查询.该方法可应用于办公楼、医院等多种室内空间.

关键词: 移动对象索引, 室内环境, 范围查询, 轨迹查询, 室内图模型

Abstract: Moving object index is widely used in location-based services. Since people spend large parts of their lives in indoor spaces (e.g. hospitals, shopping malls, subway systems, etc.), effective management of indoor mobile data becomes very important. Existing indoor moving object indices focus on historical data queries, and only one type of queries is supported. In this paper, we propose a novel index, called MQII (multiple queries indoor index), which supports not only history queries and present queries, but also object queries and range queries. MQII is based on graph-based model, and can index two aspects with the object list and bucket list structure, such as the object and spatial-temporal scales. In order to improve the query performance, we present a RFID (radio frequency identification) data preprocessing method to reduce the size of the input data sets for MQII. Furthermore, effective update and query algorithms are developed. Experimental results show that compared with existing indoor moving object indices, the data preprocessing can reduce the amount of data. In addition, the index we proposed not only supports history queries and present queries, but also provides efficient object location queries, trajectory queries and range queries. This method can be used in various indoor spaces such as office buildings, hospitals and hotels.

Key words: moving object index, indoor space, range query, trajectory query, indoor graph-based model

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