Exploring the Fusion Potentials of Data Visualization and Data Analytics in the Process of Mining Digitalization
Ruiyu Liang, Chaoran Huang, Chengguo Zhang, Binghao Li, Serkan Saydam, Ismet Canbulat
Abstract
Mining digitalisation have been receiving significant attention due to the utilisation of advanced technologies, such as IoT, automation, and sensing. However, maximising the potential value of collected data in the mining industry remains a challenge. Therefore, this paper aims to review timely concern topics to facilitate the fusion implementation in mining engineering. Specifically, this review covers recent popular topics, such as, data visualisation, data management, data analytics, data fusion, visual analytics, and mining digital twin construction. In this paper, we aim to draw a comprehensive picture about the fusion of data visualisation and analytics in the big data context, by examining the recent academic research related to these topics. Therefore, this paper reviews the visualisation domain by conventional classification, including scientific visualisation, information visualisation, and visual analytics, associated with the analysis of current digital twin development. Next, according to the challenges and issues related to visualisation development, this paper reviews the data management and data analytics domains as well. Incorporating with the fusion concept, machine learning-oriented fusion applications and potential scenarios in the mining industry have been discussed. In addition, based on the observation across various domains, this paper presents challenges and future potentials of data fusion in mining.