Informatics Publishing Limited

A. Mary Posonia ¹, V. L. Jyothi ²

Affiliations
1 Department of Computer Science and Engineering, Sathyabama University, Chennai, IN
2 Department of Computer Science and Engineering, Jeppiaar Engineering College,Chennai, IN

Abstract

Text classification is the process of automatically sorting a set of documents into categories from a predefined set. Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text classification. After pre-processing, the document can be clustered in the schema level based on the occurrence of the words relatively. Clustering process group the words based on the pattern. In proposing a feature clustering mechanism finds the pattern match with the number of relevant data present in the database.

Keywords

Feature Clustering, Feature Selection, Information Retrieval, Text Mining

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R. K. Saranya ¹, V. L. Jyothi ²

Affiliations
1 Sathyabama University, Chennai -600119, Tamil Nadu, IN
2 CSE Department, Jeppiaar Engineering College, Chennai -600119, Tamil Nadu, IN

Abstract

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.

Keywords

Big Data, Hadoop, Map Reduce, Recommender System, Web Service.

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M. Saravanan ¹, V. L. Jyothi ²

Affiliations
1 Department of Computer Science and Engineering, Sathyabama University, Chennai - 600119, IN
2 Department of Computer Science and Engineering, Jeppiaar Engineering College, Chennai – 600119, Tamil Nadu, IN

Abstract

Normal social media sites like flickr, amazon are continuing expanding giving the components that transferring the pictures, imparting the pictures to different sorts of annotations like gatherings and labels. This sites information bases comprises mass measure of pictures which has been transferred by different clients with different annotations. Consequently recovery of the pictures from that rich data heterogeneous systems relying on the client asked for inquiry and positioning the pictures as per the question likewise somewhat difficult assignment. In this paper, we are presenting another idea called heterogeneous picture rich data systems and answer for the current issues in the sites which are expressed previously. To accomplish the answer for existing issue we are proposing two calculations to be specific hmok-simrank calculation for likeness checking and positioning of the pictures so as to store it in the database, and iwsl calculation for incorporation which will be clarified in the paper about its utilization and its execution contrasted with existing calculations. We contrasted our outcome and the datasets of google and flickr, it demonstrates a huge execution than existing framework, this locales turn out to be as rich data systems with heterogeneous pictures. Presently the issue comes into picture while.

Keywords

Images, IWSL, Implement, Module, Rich Data, Similarity.

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