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: 5863
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.
@article{title="Finding map regions with high density of query keywords",
author="Zhi Yu, Can Wang, Jia-jun Bu, Xia Hu, Zhe Wang, Jia-he Jin",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="10",
pages="1543-1555",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1600043"
}
%0 Journal Article
%T Finding map regions with high density of query keywords
%A Zhi Yu
%A Can Wang
%A Jia-jun Bu
%A Xia Hu
%A Zhe Wang
%A Jia-he Jin
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 10
%P 1543-1555
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1600043
TY - JOUR
T1 - Finding map regions with high density of query keywords
A1 - Zhi Yu
A1 - Can Wang
A1 - Jia-jun Bu
A1 - Xia Hu
A1 - Zhe Wang
A1 - Jia-he Jin
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 10
SP - 1543
EP - 1555
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1600043
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.
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