The Generalized Z-Entropy’s Fractal Dimension within the Context of the Rényian Formalism Applied to a Stable M/G/1 Queue and the Fractal Dimension’s Significance to Revolutionize Big Data Analytics

The current study that explores the Generalised Z-Entropy’s fractal dimension within a non-time-dependent M/G/1 queuing system. Through numerical tests, the researchers analyse how the generated fractal dimension aligns with the specifications of the Generalised Z-Entropy. This investigation aims to enhance understanding of the relationship between entropy, complexity, and fractal geometry, with potential implications for Big Data Analytics. By combining information theory and fractal geometry, this work provides a significant generalisation in the literature on the relationship between entropy and complexity. More importantly, it is emphasised how important the fractal dimension is to the advancement of Big Data Analytics (BDAs). In addition, it also highlights the importance of the fractal dimension in advancing Big Data Analytics and mentions that the research includes unresolved questions and outlines the next steps for further investigation.