A search engine selection methodology

Commercial search engines, especially meta-search engines, were designed to locate the information by querying multiple conventional search engines and integrating the partial results generated by each search engine. A few meta-search engines attempt to intelligently select the search engines that perform best for a particular query. The problem of finding the search engine that performs best for a query is known as the search engine selection problem. This paper proposes a new solution to the search engine selection problem. The proposed solution borrows the summary schemas model from the multidatabase arena. It also utilizes user feedback and past performance to improve upon the future performance. A simulator was developed to show the validity of the model and to test its performance against solutions embedded in the design of some meta-search engines. Simulation results are presented and analyzed.