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 super winning, those significant se's do have critical barriers. This ebook introduces large-scale metasearch engine know-how, which has the capability to beat the constraints of the search engines. primarily, a metasearch engine is a seek process that helps unified entry to a number of current se's through passing the queries it gets to its part se's and aggregating the again effects right into a unmarried ranked record. A large-scale metasearch engine has hundreds of thousands or extra part se's. whereas metasearch engines have been at the beginning influenced through their skill to mix the hunt insurance of a number of se's, there also are different merits corresponding to the aptitude to acquire larger and more energizing effects and to arrive the Deep internet. the next significant parts of large-scale metasearch engines may be mentioned intimately during this booklet: seek engine choice, seek engine incorporation, and end result merging. hugely scalable and automatic strategies for those parts 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: creation / Metasearch Engine structure / seek Engine choice / seek Engine Incorporation / consequence Merging / precis and destiny examine
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Additional resources for Advanced Metasearch Engine Technology
In this chapter, we will provide some general discussions about the metasearch engine technology. 1, a reference metasearch engine architecture will be introduced. This architecture contains all the main system components and the function of each component will be described. 2, an in-depth analysis of the advantages and disadvantages of the metasearch engine technology in comparison to the search engine technology will be provided. This analysis aims to make a convincing argument that the metasearch engine technology and, especially large-scale metasearch engine technology, has nice unique features that search engines do not have, and it can play an important role in the increasingly important Web search landscape.
The retrieval results can be summarized into useful knowledge for search engine selection. , 2002). The weakness of static learning is its inability to adapt to the changes in search engine content and user query patterns. , 2002). The problem with dynamic learning is that it may take a long time to accumulate enough knowledge for effective search engine selection. Static learning and dynamic learning can be combined to form a combined-learning approach. In such 40 3. SEARCH ENGINE SELECTION an approach, initial knowledge is obtained using training queries, but the knowledge is continuously updated based on real user queries.
It is only capable of providing a very general idea on what the search engine’s document collection is about. , 1994), the representative of a document collection is generated using a pre-defined template. html Description: Information on the Perl Programming Language. Includes a local Hypertext Perl Manual, and the latest FAQ in Hypertext. uk When determining how suitable document collections are for a user query, the collections are ranked based on how well their representatives match against the query.
Advanced Metasearch Engine Technology by Weiyi Meng, Clement Yu, M. Tamer Ozsu