Advanced Metasearch Engine Technology by Weiyi Meng, Clement Yu, M. Tamer Ozsu

By Weiyi Meng, Clement Yu, M. Tamer Ozsu

One of the seek instruments at the moment on the internet, se's are the main renowned due to the recognition of significant se's reminiscent of Google and Yahoo!. whereas tremendous profitable, those significant se's do have severe barriers. This booklet introduces large-scale metasearch engine know-how, which has the capability to beat the restrictions of the various search engines. basically, a metasearch engine is a seek method that helps unified entry to a number of present se's via passing the queries it gets to its part se's and aggregating the lower back effects right into a unmarried ranked checklist. A large-scale metasearch engine has hundreds of thousands or extra part se's. whereas metasearch engines have been at first influenced through their skill to mix the hunt assurance of a number of se's, there also are different merits resembling the capability to procure greater and more energizing effects and to arrive the Deep net. the subsequent significant elements of large-scale metasearch engines can be mentioned intimately during this publication: seek engine choice, seek engine incorporation, and end result merging. hugely scalable and automatic options for those parts are emphasised. The authors make a robust case for the viability of the large-scale metasearch engine know-how as a aggressive know-how for net seek. desk of Contents: creation / Metasearch Engine structure / seek Engine choice / seek Engine Incorporation / consequence Merging / precis and destiny examine

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All search engine selection algorithms follow the following general methodology. First, they summarize the content of each search engine into its content representative. Second, when a user query comes, it is compared with the representatives of all search engines to estimate which search engines are most likely to return useful results to the query. A large number of approaches have been proposed to tackle the search engine selection problem. These approaches differ in the representatives they use to summarize the content of each search engine’s document collection, the measures they use to reflect the usefulness of each search engine with respect to a given query, and the algorithms they employ to estimate the usefulness.

1: Metasearch Engine System Architecture. results for his/her query from a metasearch engine with 1,000 component search engines. Since the 50 best results will be contained in no more than 50 component search engines, it is clear that at least 950 component search engines are useless for this particular query. Passing a query to useless search engines may cause serious problems for efficiency. Generally, sending a query to useless search engines will cause waste of resources to the metasearch engine server, each of the involved search engine servers and the Internet.

It typically takes from several days to couple of weeks for newly updated or added contents to be crawled or re-crawled. Consequently, the contents indexed by major search engines are usually several days out-of-date on average. In contrast, it is much easier for specialized search engines to maintain the freshness of their contents because they use much smaller document collections and their contents are often stored on local servers. Therefore, general-purpose metasearch engines implemented using the large-scale metasearch engine approach has a better chance to retrieve more up-to-date information than major search engines and metasearch engines that are built with major search engines.

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