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A Reduction of Online Execution Time with Sparql Query Method on Mapreduce for Bigdata Applications

IJCSEC Front Page
With the upcoming deluge amount of data the number of services are emerging on internet. The retrieval of user input from web is difficult . To overcome this challenges a new approach is proposed, which is the collaborative filtering (Club-CF) and description logic based matching technique is used to solve the matching problem. Its role aim at recruiting the similar services in the same clusters of recommended services collaboratively. This approach is achieved using two stages like all the available services divided into an small-scale cluster and then the collaborative filtering algorithm is imposed on clusters. Now the number of services in clusters which is comparatively much less than the services available on web. As a result thus approach helps to reduce the online execution time of collaborative filtering. So the user recommended services were easily extracted from the database.

Keywords:Description logic, Collaborative filtering, Feature selection, Clustering


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