Institutional Repository
| Crawling Hidden Objects with kNN Queries | |
| Yan, H; Gong, ZG; Zhang, N; Huang, T; Zhong, H; Wei, J | |
| 2016 | |
| Source | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
 |
| ISSN | 1041-4347 |
| Volume | 28Issue:4Pages:912-924 |
| English Abstract | 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.; 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. |
| Indexed Type | SCI |
| Keyword | Hidden Databases Data Crawling Location Based Services Knn Queries |
| Department | Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei, Peoples R China. Univ Macau, Macau, Peoples R China. Chinese Acad Sci, Inst Software, Beijing, Peoples R China. Univ Macau, Dept Comp & Informat Sci, Macau, Peoples R China. George Washington Univ, Dept Comp Sci, Washington, DC 20052 USA. Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing, Peoples R China. Chinese Acad Sci, Inst Software, Technol Ctr Software Engineer, Beijing, Peoples R China. |
| Language | 英语 |
| WOS ID | WOS:000372543500006 |
| Citation statistics | |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/17340 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation 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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| 07335622.pdf（864KB） | 开放获取 | License | Application Full Text | |||
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