[1]孟祥福,张霄雁,赵路路,等.基于位置-文本关系的空间对象top-k查询与排序方法[J].智能系统学报,2020,15(2):235-242.[doi:10.11992/tis.201808011]
MENG Xiangfu,ZHANG Xiaoyan,ZHAO Lulu,et al.A location-text correlation-based top-k query and ranking approach for spatial objects[J].CAAI Transactions on Intelligent Systems,2020,15(2):235-242.[doi:10.11992/tis.201808011]
点击复制
MENG Xiangfu,ZHANG Xiaoyan,ZHAO Lulu,et al.A location-text correlation-based top-k query and ranking approach for spatial objects[J].CAAI Transactions on Intelligent Systems,2020,15(2):235-242.[doi:10.11992/tis.201808011]
基于位置-文本关系的空间对象top-k查询与排序方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
15
期数:
2020年第2期
页码:
235-242
栏目:
学术论文—自然语言处理与理解
出版日期:
2020-03-05
- Title:
- A location-text correlation-based top-k query and ranking approach for spatial objects
- Keywords:
- spatial database; spatial keyword query; location-text correlation; probability density; representative object selection; top-k query and ranking
- 分类号:
- TP311.1
- 摘要:
- 针对普通的空间关键字查询通常会导致多查询结果的问题。本文提出了一种基于空间对象位置-文本相关度的top- k 查询与排序方法，用于获取与给定空间关键字查询在文本上相关且位置上相近的典型空间对象。该方法分为离线处理和在线查询处理2个阶段。在离线阶段，根据空间对象之间的位置相近性和文本相似性，度量任意一对空间对象之间的位置-文本关系紧密度。在此基础上，提出了基于概率密度的代表性空间对象选取算法，根据空间对象之间的位置-文本关系为每个代表性空间对象构建相应的空间对象序列。在线查询处理阶段，对于一个给定的空间关键字查询，利用Cosine相似度评估方法计算查询条件与代表性空间对象之间的相关度，然后使用阈值算法（threshold algorithm，TA）在预先创建的空间对象序列上快速选出top- k 个满足查询需求的典型空间对象。实验结果表明：提出的空间对象top- k 查询与排序方法能够有效地满足用户查询需求，并且具有较高的准确性、典型性和执行效率。
- Abstract:
- Due to the large size of spatial databases, a common spatial keyword query often leads to the problem of too many answers. To deal with this problem, this paper proposes a location-text correlation-based top- k query and ranking approach for spatial objects, which aims to find the typical spatial objects with high text relevancy and location proximity. This approach consists of offline processing and online query steps. The offline step scores the relationship between any pair of spatial objects by considering their location proximity and text similarity. Then, by using a probabilistic density-based representative spatial object selection method, a set of representatives over the spatial objects is selected to build a corresponding spatial object sequence. In the online query period, when a user issues a spatial keyword query, the location-text correlation between the query and representative objects is evaluated, and then, the top- k typical relevant objects can be expeditiously picked using the threshold algorithm (TA) algorithm over the sequences corresponding to representative spatial objects. The experiments demonstrate that the proposed top- k query and ranking approach can closely meet users’ needs, with high precision, typicality, and good performance.
- 参考文献/References:
-
[1] QI Jianzhong, ZHANG Rui, JENSEN C S. Continuous spatial query processing: a survey of safe region based techniques[J]. ACM computing surveys, 2018, 51(3): 64.
[2] CONG Gao, JENSEN C S. Querying geo-textual data: spatial keyword queries and beyond[C]//Proceedings of 2016 International Conference on Management of Data. San Francisco, California, USA, 2016: 2207-2212.
[3] ZHENG Kai, SU Han, ZHENG Bolong, et al. Interactive top-k spatial keyword queries[C]//Proceedings of the 31st International Conference on Data Engineering. Seoul, South Korea, 2015: 423-434.
[4] LU Ying, LU Jiaheng, CONG Gao, et al. Efficient algorithms and cost models for reverse spatial-keyword k-nearest neighbor search[J]. ACM transactions on database systems, 2014, 39(2): 13.
[5] DE FELIPE I, HRISTIDIS V, RISHE N. Keyword search on spatial databases[C]//Proceedings of the 24th IEEE International Conference on Data Engineering. Cancun, Mexico, 2008: 656-665.
[6] LI Guoliang, XU Jing, FENG Jianhua. Keyword-based k-nearest neighbor search in spatial databases[C]//Proceedings of the 21st ACM International Conference on Information and Knowledge Management. Maui, Hawaii, USA, 2012: 2144-2148.
[7] WANG Xiang, ZHANG Ying, ZHANG Wenjie, et al. Skype: top-k spatial-keyword publish/subscribe over sliding window[J]. Proceedings of the VLDB endowment, 2016, 9(7): 588-599.
[8] ZHANG Dongxiang, CHEE Y M, MONDAL A, et al. Keyword search in spatial databases: towards searching by document[C]//Proceedings of the 25th International Conference on Data Engineering. Shanghai, China, 2009: 688-699.
[9] CONG Gao, JENSEN C S, WU Dingming. Efficient retrieval of the top-k most relevant spatial web objects[J]. Proceedings of the VLDB endowment, 2009, 2(1): 337-348.
[10] CHEN Lei, LIN Xin, HU Haibo, et al. Answering why-not questions on spatial keyword top-k queries[C]//Proceedings of the 31st International Conference on Data Engineering. Seoul, South Korea, 2015: 279-290.
[11] KWON H Y, WANG Haixun, WHANG K Y. G-index model: a generic model of index schemes for top-k spatial-keyword queries[J]. World wide web, 2015, 18(4): 969-995.
[12] GUTTMAN A. R-trees: a dynamic index structure for spatial searching[C]//Proceedings of 1984 ACM SIGMOD International Conference on Management of Data. Boston, Massachusetts, 1984: 47-57.
[13] BECKMANN N, KRIEGEL H P, SCHNEIDE R, et al. The R*-tree: an efficient and robust access method for points and rectangles[C]//Proceedings of 1990 ACM SIGMOD International Conference on Management of Data. Atlantic City, New Jersey, USA, 1990: 322-331.
[14] ZHANG Chengyuan, ZHANG Ying, ZHANG Wenjie, et al. Inverted linear quadtree: efficient top k spatial keyword search[J]. IEEE transactions on knowledge and data engineering, 2016, 28(7): 1706-1721.
[15] ZHOU Yinghua, XIE Xing, WANG Chuang, et al. Hybrid index structures for location-based Web search[C]//Proceedings of the 14th ACM International Conference on Information and Knowledge Management. Bremen, Germany, 2005: 155-162.
[16] LI Zhisheng, LEE K C K, ZHENG Baihua, et al. IR-Tree: an efficient index for geographic document search[J]. IEEE transactions on knowledge and data engineering, 2011, 23(4): 585-599.
[17] HONG H J, CHIU G M, TSAI W Y. A single quadtree-based algorithm for top-k spatial keyword query[J]. Pervasive and mobile computing, 2017, 42: 93-107.
[18] HUA Ming, PEI Jian, FU A W C, et al. Top-k typicality queries and efficient query answering methods on large databases[J]. The VLDB journal, 2009, 18(3): 809-835.
[19] FAGIN R, LOTEM A, NAOR M. Optimal aggregation algorithms for middleware[J]. Journal of computer and system sciences, 2003, 66(4): 614-656.
备注/Memo
收稿日期:2018-08-12。
基金项目:国家自然科学基金面上项目(61772249)；辽宁省自然科学基金项目(20170540418)；辽宁省教育厅科学研究项目(LJYL018)
作者简介:孟祥福，教授，博士生导师，主要研究方向为空间关键字查询、大数据分析与可视化、机器学习算法。主持国家自然科学基金项目2项、辽宁省各类基金项目3项。发表学术论文30余篇;张霄雁，工程师，主要研究方向为空间数据查询与分析、城市计算、机器学习算法。主持辽宁省教育厅科研项目1项。发表学术论文10余篇;赵路路，硕士研究生，主要研究方向为空间数据查询与分析。
通讯作者:孟祥福.E-mail:marxi@126.com
更新日期/Last Update:
1900-01-01