L1 Norm SVD-Based Ranking Scheme: A Novel Method in Big Data Mining

Scientometrics deals with analyzing and quantifying works in science, technology, and innovation. It is a study that focuses on quality rather than quantity. The journals are evaluated against several different metrics such as the impact of the journals, scientific citation, SJR, SNIP indicators as well as the indicators used in policy and management context. The practice of using journal metrics for evaluation involves handling a large volume of data to derive useful patterns and conclusions. These metrics play an important role in the measurement and evaluation of research performance. Due to the fact that most metrics are being manipulated and abused, it becomes essential to judge and evaluate a journal by using a single metric or a reduced set of significant metrics. We propose \(l_1\)-norm singular value decomposition (\(l_1\)-SVD) to efficiently solve this problem. We evaluate our method to study the emergence of a new journal, Astronomy and Computing, by comparing it with 46,000 journals chosen from the fields of computing, informatics, astronomy, and astrophysics.