东北大学学报:自然科学版 ›› 2017, Vol. 38 ›› Issue (6): 793-797.DOI: 10.12068/j.issn.1005-3026.2017.06.007

• 信息与控制 • 上一篇    下一篇

基于位置的偏好查询处理技术

李淼, 谷峪, 于戈   

  1. (东北大学 计算机科学与工程学院, 辽宁 沈阳110169)
  • 收稿日期:2015-05-24 修回日期:2015-05-24 出版日期:2017-06-15 发布日期:2017-06-11
  • 通讯作者: 李淼
  • 作者简介:李淼(1985-),女,辽宁鞍山人,东北大学博士研究生; 谷峪(1981-),男,辽宁鞍山人,东北大学教授; 于戈(1963-),男,辽宁沈阳人,东北大学教授,博士生导师.
  • 基金资助:

    国家自然科学基金资助项目(61472071, 61433008); 国家重点基础研究发展计划项目(2012CB316201).

A Technique for Processing Location-aware Preference Queries

LI Miao, GU Yu, YU Ge   

  1. School of Computer Science & Engineering, Northeastern University, Shenyang 110169, China.
  • Received:2015-05-24 Revised:2015-05-24 Online:2017-06-15 Published:2017-06-11
  • Contact: YU Ge
  • About author:-
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摘要:

在线位置服务技术日益普及,用户能够很容易获得他们的地理位置信息.随之产生了各类有关空间关键字的查询,这些查询可以提供定位服务的基本查询功能.研究了基于位置的偏好查询处理技术,旨在为用户找到一个目的地,找到的结果应该满足指定的特性,并且靠近满足用户提出的偏好.同时,提出一种新颖的查询框架,该框架通过对IR-tree的节点扩展给出预计算信息表,根据扩展的IR-tree能够减少搜索空间并提出准确计算方法来有效地回答基于位置的偏好查询.在真实数据集上进行实验验证了提出方法的有效性.

关键词: 偏好查询, IR-树, 扩展IR-树, 倒排文件, 位置服务

Abstract:

There has been increasing popularity of online location-based services. It gives prominence to various types of spatial-keyword queries, which are employed to provide fundamental querying functionality for location-based services. A technique for processing location-aware preference queries was studied that aimed to find a destination place for a user. The user wants to go to a place labeled with a specified category feature (e.g., hotel), and he/she has a location and a set of additional preferences. It was expected that the result place of the query belongs to the specified feature, and it was close to places satisfying the preferences of the user. A novel framework was developed for answering the queries, which was called augmented IR-tree. An augmented IR-tree could be obtained by adding the pre-computed information into an IR-tree. The augmented IR-tree could be used to reduce the search space and compute the exact query result. The proposed technique was verified by extensive experiments on one real dataset, and the technique is more efficient than baseline methods.

Key words: preference query, IR-tree, augmented IR-tree, inverted files, location-based service

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