Retrieval of Notable Academic People by an Ameliorated Skyline Operator

Abstract

Our target issue in this study is to discover notable academic people in higher education by assessment of research and educational impacts. While open data that can be applied to such an analysis is available, conventional retrieval methods have shortcomings in the search because of the characteristics of the target data. To tackle this problem, we contrive a new method for discerning notable academic people who transcend other people. Our proposed method is a hybrid method of conventional methods and exerts the advantages of the conventional methods. It is particularly important to identify excellent people who have huge impacts in scientific research and university education when promoting meaningful activities in higher education, such as open science and faculty development. In the experiments in this study, numerical attributes contained in the KAKEN and CiNii Books databases are used to quantify the impact on scientific research and university education. By observing the experimental results, we have confirmed that the proposed method succeeds in overcoming the deficiencies of the conventional retrieval methods.