计算机科学 ›› 2018, Vol. 45 ›› Issue (4): 182-189.doi: 10.11896/j.issn.1002-137X.2018.04.031

• 软件与数据库技术 • 上一篇    下一篇

用户偏好约束的空间关键词范围查询处理方法

郭帅,刘亮,秦小麟   

  1. 南京航空航天大学计算机科学与技术学院 南京210016,南京航空航天大学计算机科学与技术学院 南京210016,南京航空航天大学计算机科学与技术学院 南京210016
  • 出版日期:2018-04-15 发布日期:2018-05-11
  • 基金资助:
    本文受国家自然科学基金项目(61373015,5),江苏省自然科学基金项目(BK20140832),中国博士后基金项目(2013M540447),江苏省博士后基金项目(1301020C)资助

Spatial Keyword Range Query with User Preferences Constraint

GUO Shuai, LIU Liang and QIN Xiao-lin   

  • Online:2018-04-15 Published:2018-05-11

摘要: 随着基于地理位置的个性化服务的广泛应用,用户偏好约束的空间关键词范围查询成为了研究热点。现有面向空间关键词范围查询的索引没有考虑用户偏好属性,导致剪枝性能和查询效率较低。为了解决该问题,提出了一种支持用户偏好属性、空间位置、关键词协同剪枝的混合索引BRPQ;并在此基础上,提出了高效的用户偏好约束的空间关键词范围查询处理算法。实验结果表明,相比现有索引,BRPQ索引的构建时间平均减少了13%,查询效率平均提升了20%。

关键词: 空间文本对象,空间关键词范围查询,用户偏好,混合索引

Abstract: With the wide application of location-based personalized service,spatial keyword range query with user pre-ferences constraint becomes a research hotspot.The existing indexes for spatial keyword range query do not take user preferences into account,resulting in poor pruning performance and low query efficiency.In order to solve these problems,a hybrid index called BRPQ(Boolean Range with Preferences Query index) was proposed to support user prefe-rences,spatial location and keywords collaborative pruning.This paper also proposed an efficient query processing algorithm for spatial keywords range query with user preferences constraint.Experimental results show that BRPQ outperforms the existing indexes in terms of building time and query processing efficiency.

Key words: Spatio-textual object,Spatial keyword range query,User preferences,Hybrid index

[1] ZHOU A Y,YANG B,JIN C Q,et al.Location-Based Services:Architecture and Progress[J].Chinese Journal of Computers,2011,34(7):1155-1171.(in Chinese) 周傲英,杨彬,金澈清,等.基于位置的服务:架构与进展[J].计算机学报,2011,34(7):1155-1171.
[2] CAO X,CHEN L,CONG G,et al.Spatial Keyword Querying[C]∥International Conference on Conceptual Modeling.Springer-Verlag,2012:16-29.
[3] CHEN L,CONG G,CAO X,et al.Temporal Spatial-Keyword Top-k publish/subscribe[C]∥International Conference on Data Engineering.IEEE,2015:255-266.
[4] WANG X,ZHANG Y,ZHANG W,et al.AP-Tree:Efficiently support continuous spatial-keyword queries over stream[C]∥ 2015 IEEE 31st International Conference on Data Engineering.2015:1107-1118.
[5] CHEN L,CONG G,CAO X.An efficient query indexing mechanism for filtering geo-textual data[C]∥ACM SIGMOD International Conference on Management of Data.2013:749-760.
[6] LIU X,CHEN L,WAN C.LINQ:A Framework for Location-Aware Indexing and Query Processing[J].IEEE Transactions on Knowledge & Data Engineering,2015,27(5):1288-1300.
[7] CHEN L,CONG G,JENSEN C S,et al.Spatial keyword query processing:an experimental evaluation[J].Proceedings of the VLDB Endowment,2013,6(3):217-228.
[8] DE FELIPE I,HRISTIDIS V,RISHE N.Keyword Search on Spatial Databases[C]∥International Conference on Data Engineering.IEEE Computer Society,2008:656-665.
[9] ZHANG D,TAN K L,TUNG A K H.Scalable top-k spatial keyword search[C]∥International Conference on Extending Database Technology.ACM,2013:359-370.
[10] ZHOU Y H,XIE X,WANG C,et al.Hybrid index structures for location-based Web search[C]∥ DBLP.2005:155-162.
[11] CONG G,JENSEN C S,WU D.Efficient retrieval of the top-k most relevant spatial web objects[J].Proceedings of the VLDB Endowment,2009,2(1):337-348.
[12] ZHANG D X,CHEE Y M,MONDAL A,et al.Keyword search in spatial databases:Towards searching by document[C]∥ International Conference on Data Engineering.IEEE,2009:688-699.
[13] LI Y H,HUANG Q,JIANG H,et al.Research on Processing Continuous Spatial Keyword Range Queries in Road Networks[J].Computer Science,2014,41(7):232-235.(in Chinese) 李艳红,黄群,蒋宏,等.路网中空间关键字连续范围查询算法研究[J].计算机科学,2014,41(7):232-235.
[14] HMEDEH Z,KOURDOUNAKIS H,CHRISTOPHIDES V,etal.Subscription indexes for web syndication systems[C]∥International Conference on Extending Database Technology.ACM,2012:312-323.
[15] MANNING C D,RAGHAVAN P,SCHTZE H.An Introduction to Information Retrieval[J].Journal of the American Society for Information Science & Technology,2008,43(3):824-825.
[16] SILBERSCHATZ A,KORTH H F,SUDARSHAN S.Database System Concepts[M].New York,USA:McGraw-Hill,2006.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 编辑部. 新网站开通,欢迎大家订阅![J]. 计算机科学, 2018, 1(1): 1 .
[2] 雷丽晖,王静. 可能性测度下的LTL模型检测并行化研究[J]. 计算机科学, 2018, 45(4): 71 -75, 88 .
[3] 夏庆勋,庄毅. 一种基于局部性原理的远程验证机制[J]. 计算机科学, 2018, 45(4): 148 -151, 162 .
[4] 厉柏伸,李领治,孙涌,朱艳琴. 基于伪梯度提升决策树的内网防御算法[J]. 计算机科学, 2018, 45(4): 157 -162 .
[5] 王欢,张云峰,张艳. 一种基于CFDs规则的修复序列快速判定方法[J]. 计算机科学, 2018, 45(3): 311 -316 .
[6] 孙启,金燕,何琨,徐凌轩. 用于求解混合车辆路径问题的混合进化算法[J]. 计算机科学, 2018, 45(4): 76 -82 .
[7] 张佳男,肖鸣宇. 带权混合支配问题的近似算法研究[J]. 计算机科学, 2018, 45(4): 83 -88 .
[8] 伍建辉,黄中祥,李武,吴健辉,彭鑫,张生. 城市道路建设时序决策的鲁棒优化[J]. 计算机科学, 2018, 45(4): 89 -93 .
[9] 刘琴. 计算机取证过程中基于约束的数据质量问题研究[J]. 计算机科学, 2018, 45(4): 169 -172 .
[10] 钟菲,杨斌. 基于主成分分析网络的车牌检测方法[J]. 计算机科学, 2018, 45(3): 268 -273 .