Crawling Hidden Objects with kNN Queries
文献类型：期刊论文
| 作者 | Yan, H ; Gong, ZG ; Zhang, N ; Huang, T ; Zhong, H ; Wei, J |
| 刊名 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
 |
| 出版日期 | 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 |
| 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. |
入库方式： OAI收割
来源：软件研究所
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