In our approach, the query space of a deep web data source is stratified based on a pilot sample. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. Therefore, traditional data mining methods cannot be directly applied. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. Existing work in outlier detection never considers the context of deep web. Xian, Xuefeng Zhao, Pengpeng Sheng, Victor S Fang, Ligang Gu, Caidong Yang, Yuanfeng Cui, Zhimingįor many applications, finding rare instances or outliers can be more interesting than finding common patterns. Stratification-Based Outlier Detection over the Deep Web. This video provides an introduction to the deep web search engine. The deep web includes content in searchable databases available to web users but not accessible by popular search engines, such as Google. To make the web work better for science, OSTI has developed state-of-the-art technologies and services including a deep web search capability.
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