As web contents grow, the importance of search engines become more critical and at the same time user satisfaction decreases. Query recommendation is a new approach to improve search results in web. In this paper a method is proposed that, given a query submitted to a search engine, suggests a list of queries that are related to the user input query. The related queries are based on previously issued queries, and can be issued by the user to the search engine to tune or redirect the search process. The proposed method is based on clustering processes in which groups of semantically similar queries are detected. The clustering process uses the content of historical preferences of users registered in the query log of the search engine. This facility provides queries that are related to the ones submitted by users in order to direct them toward their required information. This method not only discovers the related queries but also ranks them according to a similarity measure. The method has been evaluated using real data sets from the search engine query log.
Branch and Bound technique is commonly used for intelligent search in finding a set of integer solutions within a space of interest. The corresponding binary tree structure provides a natural parallelism allowing concurrent evaluation of subproblems using parallel computing technology. While the master-worker paradigm is successfully used in many parallel applications as a common framework to implement parallel applications, it has drawbacks when a large number of computing resources are connected via WAN. A supervisor-master-sub-master-worker algorithm has been proposed. From the solved benchmark example this algorithm proved to provide a considerable save of time. Results show that a consistently better efficiency can be achieved in solving integer equations, providing reduction of time. The hierarchical supervisor-master-sub-master-worker algorithm sustains good performance revealed from the knapsack problem solved as a benchmark example.
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