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Memory Optimization Using Genetic Algorithm of Relational Keyword Search Techniques

A.Ravichandiran, A.Vijayan, K.Ravikumar

Currently the relational keyword based searches techniques consider the large number of data’s to provide efficient result while the user searching. There is an issue of limited memory hence there is a need of the implementation of the novel techniques/ algorithm. To improve the search technique process by optimizing the query from that has to attain the memory optimization with the help of the genetic algorithm. Here the genetic algorithm plays the major role to select the optimized query to execute in the final result execution process based on the user given query. Here the process is executed in the dynamic manner which is considered as the real time scenario in that have to execute the whole process as the dynamic based on the user given query. The proposed system called MOGA. It means Memory Optimization with Genetic Algorithm. Here the memory is optimized based on the user given query to search the particular term. Then the User search goal is attained by applying the efficient clustering technique called Fuzzy C Means Clustering on the Clicked URL Data. The performance will be executed in terms of the space consumption and the time complexity of the searching process. In the past decade, extending the keyword search paradigm to relational data has been an active area of research within the database and information retrieval (IR) community. A large number of approaches have been proposed and implemented, but despite numerous publications, there remains a severe lack of standardization for system evaluations.