Crawling Hidden Objects with kNN Queries

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	Crawling Hidden Objects with kNN Queries
	Yan, H ; Gong, ZG ; Zhang, N ; Huang, T ; Zhong, H ; Wei, J
刊名	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
	2016
卷号	28 期号:4 页码:912-924
关键词	Hidden databases data crawling location based services kNN queries
ISSN号	1041-4347
中文摘要	Many websites offering Location Based Services (LBS) provide a kNN search interface that returns the top-k nearest-neighbor objects (e.g., nearest restaurants) for a given query location. This paper addresses the problem of crawling all objects efficiently from an LBS website, through the public kNN web search interface it provides. Specifically, we develop crawling algorithm for 2D and higher-dimensional spaces, respectively, and demonstrate through theoretical analysis that the overhead of our algorithms can be bounded by a function of the number of dimensions and the number of crawled objects, regardless of the underlying distributions of the objects. We also extend the algorithms to leverage scenarios where certain auxiliary information about the underlying data distribution, e.g., the population density of an area which is often positively correlated with the density of LBS objects, is available. Extensive experiments on real-world datasets demonstrate the superiority of our algorithms over the state-of-the-art competitors in the literature.
英文摘要	Many websites offering Location Based Services (LBS) provide a kNN search interface that returns the top-k nearest-neighbor objects (e.g., nearest restaurants) for a given query location. This paper addresses the problem of crawling all objects efficiently from an LBS website, through the public kNN web search interface it provides. Specifically, we develop crawling algorithm for 2D and higher-dimensional spaces, respectively, and demonstrate through theoretical analysis that the overhead of our algorithms can be bounded by a function of the number of dimensions and the number of crawled objects, regardless of the underlying distributions of the objects. We also extend the algorithms to leverage scenarios where certain auxiliary information about the underlying data distribution, e.g., the population density of an area which is often positively correlated with the density of LBS objects, is available. Extensive experiments on real-world datasets demonstrate the superiority of our algorithms over the state-of-the-art competitors in the literature.
收录类别	SCI
语种	英语
WOS记录号	WOS:000372543500006
公开日期	2016-12-09
内容类型	期刊论文
源URL	[http://ir.iscas.ac.cn/handle/311060/17340]
专题	软件研究所_软件所图书馆_期刊论文
推荐引用方式 GB/T 7714	Yan, H,Gong, ZG,Zhang, N,et al. Crawling Hidden Objects with kNN Queries[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2016,28(4):912-924.
APA	Yan, H,Gong, ZG,Zhang, N,Huang, T,Zhong, H,&Wei, J.(2016).Crawling Hidden Objects with kNN Queries.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,28(4),912-924.
MLA	Yan, H,et al."Crawling Hidden Objects with kNN Queries".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 28.4(2016):912-924.