Pareto Principle to Improve Anomaly Detection on Software Asset Management

Abstract

Software Asset Management (SAM) is essential for a large company with a centralized software distribution system. Unfortunately, the operationalization of SAM has various problems. These problems become even more complicated when it comes to managing big data. This research proposes the Pareto Principle to reduce data dimensions to solve the problem of large data sizes without losing dataset characteristics before conducting anomaly detection. This anomaly detection is mandatory to identify and reduce invalid data due to misalignment or misclassification. Therefore, this study compares the state-of-arts anomaly detection algorithms: I-forest, KNN, and SVM. As a result, we found that SVM is the best algorithm, with an accuracy rate of 78.4%. In addition, using Pareto for the total population and software name variation effectively reduces the number of observations to 20% data instances with only 16.5% features without compromising the dataset characteristics. In the best algorithm based on experimental results, the use of Pareto increases accuracy by 3.2% and processing time efficiency by 20%.