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SSC Based RS: An Efficient Service Recommendation System for Handling Big Data Applications
Objectives: To find an appropriate web service and reduce the time taken for introducing web service, to improve parallel processing, to reduce the complexity and to improve its scalability and efficiency in big data environment. Methods: MapReduce framework in Hadoop platform is for increase the efficiency and scalability in big data domain, SSC based RS, web service information are structured in hierarchical format. The proposed system calculates the semantic comparison between the big data applications. Findings: SSC based RS (Semantic Similarity Calculation Based Recommendation System) is used to efficiently suggest better services for the requested users, by using semantic dictionary the semantic similarity will be calculated. Here, the services are stored in the hierarchical structure will increase the recommendation process faster. An experimental result shows that our proposed algorithm provides a suitable recommended service compared to other existing approaches. Applications/Improvement: In Big Data the proposed technique improves the efficiency and scalability by applying MapReduce parallel processing standard on Hadoop environment.
Big Data, Hadoop, Map Reduce, Recommender System, Web Service.
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