This paper presents the QA-Pagelet as a fundamental data preparation technique for large scale data analysis of the Deep Web. To support QA-Pagelet extraction, we present the Thor framework for sampling, locating, and partioning the QA-Pagelets from the Deep Web. Two unique features of the Thor framework are (1) the novel page clustering for grouping pages from a Deep Web source into distinct clusters of control-flow dependent pages; and (2) the novel subtree filtering algorithm that exploits the structural and content similarity at subtree level to identify the QA-Pagelets within highly ranked page clusters. We evaluate the effectiveness of the Thor framework through experiments using both simulation and real datasets. We show that Thor performs well over millions of Deep Web pages and over a wide range of sources, including eCommerce sites, general and specialized search engines, corporate websites, medical and legal resources, and several others. Our experiments also show that the proposed page clustering algorithm achieves low-entropy clusters, and the subtree filtering algorithm identifies QA-Pagelets with excellent precision and recall.

Abstract—This paper presents document search model based on its visual content. There is used hierarchical clustering algorithm- GHSOM. Description of proposed model is given as learning and searching phase. Also some experiments are described on benchmark image sets (e.g. ICPR, MIRFlickr) and created document set. Paper presents some experiments connected with document measures and their influence on searching results. Also in this paper some first results are given and directions of further research are given. T I.