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CLC number: TP391

On-line Access: 2017-12-04

Received: 2016-01-27

Revision Accepted: 2016-06-30

Crosschecked: 2017-11-03

Cited: 0

Clicked: 2012

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Can Wang

http://orcid.org/0000-0002-5890-4307

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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.10 P.1543-1555

http://doi.org/10.1631/FITEE.1600043


Finding map regions with high density of query keywords


Author(s):  Zhi Yu, Can Wang, Jia-jun Bu, Xia Hu, Zhe Wang, Jia-he Jin

Affiliation(s):  Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   yuzhirenzhe@zju.edu.cn, wcan@zju.edu.cn, bjj@zju.edu.cn, huxia@hznet.com.cn, wangzhe89@zju.edu.cn, jinjiahe@zju.edu.cn

Key Words:  Map search, Region search, Region recommendation, Spatial keyword search, Geographic information system, Location-based service


Zhi Yu, Can Wang, Jia-jun Bu, Xia Hu, Zhe Wang, Jia-he Jin. Finding map regions with high density of query keywords[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(10): 1543-1555.

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author="Zhi Yu, Can Wang, Jia-jun Bu, Xia Hu, Zhe Wang, Jia-he Jin",
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pages="1543-1555",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1600043"
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Abstract: 
We consider the problem of finding map regions that best match query keywords. This region search problem can be applied in many practical scenarios such as shopping recommendation, searching for tourist attractions, and collision region detection for wireless sensor networks. While conventional map search retrieves isolate locations in a map, users frequently attempt to find regions of interest instead, e.g., detecting regions having too many wireless sensors to avoid collision, or finding shopping areas featuring various merchandise or tourist attractions of different styles. Finding regions of interest in a map is a non-trivial problem and retrieving regions of arbitrary shapes poses particular challenges. In this paper, we present a novel region search algorithm, dense region search (DRS), and its extensions, to find regions of interest by estimating the density of locations containing the query keywords in the region. Experiments on both synthetic and real-world datasets demonstrate the effectiveness of our algorithm.

地图关键字密集区域搜索技术

概要:根据查询关键字找到相应地图区域,可以应用于购物推荐、旅游景点检索、无线传感网络冲突干扰区域检测等诸多场景。传统地图检索一般返回若干个兴趣点,而用户经常希望找到感兴趣的区域,比如不同风格的购物区域、推荐旅游景点区域,或查找无线节点最密集区域以避免无线网络冲突,等。由于区域形状不确定性等原因,地图区域检索是一个具有高度挑战性的问题。本文提出一种不规则区域检索算法DRS(dense region search)及其扩展形式,可高效预估搜索区域关键字密度,从而快速找到感兴趣的区域。在模拟和真实数据集上的试验证明,算法在不规则密集地图区域检索任务时有效。

关键词:地图搜索;区域搜索;区域推荐;空间关键字搜索;地理信息系统;基于位置的服务

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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