Research on Deep Web Query Interface Clustering Based on Hadoop
暂无分享,去创建一个
Wei Li | Qian He | Rui Zhang | Yufeng Wang | Sai Wang | Baohua Qiang
[1] Peng Jiang,et al. Multi-objective optimization integration of query interfaces for the Deep Web based on attribute constraints , 2013, Data Knowl. Eng..
[2] B. Huberman,et al. The Deep Web : Surfacing Hidden Value , 2000 .
[3] Tim Furche,et al. OXPath: A language for scalable data extraction, automation, and crawling on the deep web , 2012, The VLDB Journal.
[4] Mitesh Patel,et al. Accessing the deep web , 2007, CACM.
[5] Gerard Salton,et al. A vector space model for automatic indexing , 1975, CACM.
[6] Tim Furche,et al. The ontological key: automatically understanding and integrating forms to access the deep Web , 2013, The VLDB Journal.
[7] Tom White,et al. Hadoop: The Definitive Guide , 2009 .
[8] Oleg V. Shylo,et al. On Maximum Speedup Ratio of Restart Algorithm Portfolios , 2013, INFORMS J. Comput..
[9] James M. Tien,et al. Big Data: Unleashing information , 2013, 2013 10th International Conference on Service Systems and Service Management.
[10] Martin Bergman,et al. The deep web:surfacing the hidden value , 2000 .
[11] Wei Liu,et al. ViDE: A Vision-Based Approach for Deep Web Data Extraction , 2010, IEEE Transactions on Knowledge and Data Engineering.
[12] Andrew Olney,et al. Generalizing Latent Semantic Analysis , 2009, 2009 IEEE International Conference on Semantic Computing.
[13] Subbarao Kambhampati,et al. Assessing relevance and trust of the deep web sources and results based on inter-source agreement , 2013, TWEB.
[14] H. Peter Hofstee,et al. Big Data text-oriented benchmark creation for Hadoop , 2013, IBM J. Res. Dev..
[15] T. Velmurugan,et al. Computational Complexity between K-Means and K-Medoids Clustering Algorithms for Normal and Uniform Distributions of Data Points , 2010 .