

FOLLOWUS
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
Hangzhou Science and Technology Information Research Institute, Hangzhou 310001, China
[ "YU Zhi, E-mail: yuzhirenzhe@zju.edu.cn" ]
Can WANG, E-mail: wcan@zju.edu.cn
[ "BU Jia-jun, E-mail: bjj@zju.edu.cn" ]
[ "HU Xia, E-mail: huxia@hznet.com.cn" ]
[ "WANG Zhe, E-mail: wangzhe89@zju.edu.cn" ]
[ "JIN Jia-he, E-mail: jinjiahe@zju.edu.cn" ]
Received:27 January 2016,
Revised:2017-11-;3,
Published:2017-10
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Zhi YU, Can WANG, Jia-jun BU, et al. Finding map regions with high density of query keywords[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(10): 1543-1555.
Zhi YU, Can WANG, Jia-jun BU, et al. Finding map regions with high density of query keywords[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(10): 1543-1555. DOI: 10.1631/FITEE.1600043.
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。
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.
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