Master's Thesis from the year 2013 in the subject Computer Science - Internet, New Technologies, grade: 1.0, Anna University (SBM COLLEGE OF ENGINEERING AND TECHNOLOGY), course: ME COMPUTER SCIENCE, language: English, abstract: When a query is submitted to a search engine, the search engine returns a dynamically generated result page containing the result records, each of which usually consists of a link to and/or snippet of a retrieved Web page. In addition, such a result page often also contains information irrelevant to the query, such as information related to the hosting site of the search engine and advertisements.In this paper, we present a technique for automatically producing wrappers that can be used to extract search result records from dynamically generated result pages returned by search engines.As the popular two-dimensional media, the contents on Web pages are always displayed regularly for users to browse.This motivates us to seek a different way for deep Web data extraction to overcome the limitations of previous works by utilizing some interesting common visual features on the deep Web pages. In this paper, a novel vision-based approach that is Web-page programming-language-independent is proposed. This approach primarily utilizes the visual features on the deep Web pages to implement deep Web data extraction, including data record extraction and data item extraction. We also propose a new evaluation measure revision to capture the amount of human effort needed to produce perfect extraction. Our experiments on a large set of Web databases show that the proposed vision-based approach is highly effective for deep Web data extraction.A meta search engine supports unified access to multiple component search engines. To build a very large-scale Meta search engine that can access up to hundreds of thousands of component search engines, one major challenge is to incorporate large numbers of autonomous search engines in a highly effective manner. To solve

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