By Weiyi Meng, Clement Yu, M. Tamer Ozsu
One of the seek instruments presently on the internet, se's are the main popular due to the recognition of significant se's equivalent to Google and Yahoo!. whereas tremendous profitable, those significant se's do have severe obstacles. This publication introduces large-scale metasearch engine expertise, 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 latest se's via passing the queries it gets to its part se's and aggregating the lower back effects right into a unmarried ranked record. A large-scale metasearch engine has millions or extra part se's. whereas metasearch engines have been firstly inspired via their skill to mix the hunt insurance of a number of se's, there also are different advantages comparable to the capability to procure higher and brisker effects and to arrive the Deep net. the next significant elements of large-scale metasearch engines can be mentioned intimately during this booklet: seek engine choice, seek engine incorporation, and consequence merging. hugely scalable and automatic recommendations for those elements are emphasised. The authors make a powerful case for the viability of the large-scale metasearch engine know-how as a aggressive expertise for net seek. desk of Contents: advent / Metasearch Engine structure / seek Engine choice / seek Engine Incorporation / outcome Merging / precis and destiny examine
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Additional resources for Advanced Metasearch Engine Technology
Such a representative is only capable of providing a very general idea on what a search engine’s content is about and, consequently, search engine selection algorithms using this type of representatives are not very accurate in estimating the true usefulness of search engines with respect to a given query. Rough representatives are often manually generated. 2. Learning-based approaches. In these approaches, the knowledge about which search engines are likely to return useful results to what types of queries is learned from past retrieval experiences.
2008). The main limitation of this technique is that it is very difficult to obtain the complete contents from deep Web search engines because it is almost impossible to use a reasonable number of queries to retrieve all the contents from a deep Web search engine. Similar to deep Web crawlers, metasearch engines interact with search engines, including deep Web search engines, through their query interfaces (including APIs). But unlike deep Web crawlers, metasearch engines pass each user query directly to search engines to retrieve contents that are related to that query only, and there is no need to obtain the whole content of any search engine in advance.
How well the content of a search engine’s document collection match a query. A good search engine selection algorithm should identify potentially useful search engines for any given query accurately and rank them based on their estimated usefulness with the more useful ones ranked ahead of the less useful ones. 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.