A Novel Approach for Analyzing the Social Network

AbstractMassive datasets are becoming more prevalent. In this paper, we propose an algorithm to process a large symmetric matrix of billion scale graph in order to extract knowledge from graph dataset. For example, interesting patterns like the people who frequently visit your page and the most number of participating triangles can be obtained using the algorithm. These interesting patterns are discovered by computation of several eigen values and eigen vectors. The main challenge in analyzing the graph data are simplifying the graph, counting the triangles, finding trusses. These challenges are addressed in the proposed algorithm by using orthogonalization, parallelization and blocking techniques. The proposed algorithm is able to run on highly scalable MapReduce environment. we use a social network dataset (facebook approximately 2 to 7 TB of data) to evaluate the algorithm. we also show experimental results to prove that the proposed algorithm scale well and efficiently process the billion scale graph

Design and Development of an Ontology based Personal Web Search Engine

AbstractAs a model for knowledge description and formalization, ontologies are widely used to represent user profiles in personalized web information gathering. However, when representing user profiles, many models have utilized only knowledge from either a global knowledge base or user local information. A personalized search engine which is a hybrid system has been proposed for personalized search and reasoning over user profiles. The hybrid system contains both the global knowledge base and the user local information. User profiles are created for every users to gather their interest and relevance. This system also learns user profiles based on which the user is provided with candidates in Ontology Learning Environment (OLE) tool. The search results are personalized and have topic specificity. The efficiency of a search application is defined by the accuracy by which the search results match the user interests. The efficiency improvement in our application can be defined by the reduction in the number of pages which a particular user who searches for a string. This search engine is evaluated by getting feedback from three kinds of users. The results show that this ontology model is successful