Enhanced bibliographic data retrieval and visualization using query optimization and spectral centrality measure

As the amount of data generated is growing exponentially, harnessing such voluminous data has become a major challenge these years especially bibliographic data. This study proposing an enhance bibliographic data retrieval and visualization using hybrid clustering method consists of K-harmonic mean (KHM) and Spectral Algorithm and eigenvector centrality measure. A steady increase of publications recorded in the Digital Bibliography and Library Project (DBLP) can be identified from year 1936 until 2018, reaching the number 4,327,507 publications. This study will be focusing on the visualization of bibliographic data by retrieving the most influenced papers using hybrid clustering techniques and visualize it in an understandable network diagram using the weight age node. This web based approach will be using Java programming language and Mongo DB (NoSQL database) to improve the retrieval performance by 80%, precision of the search result of the bibliographic data by omitting non-significance papers and visualizing a clearer network diagram using centrality measure for better decision making. This method will make ease for the young researchers, educators and students to dive into the enormous real world social and biological network.

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