The concept of Big Data refers to data sets that are so large, variable and/or complex that traditional data processing applications struggle to integrate the data for effective investigation, analysis and value generation. With advances in sensor technology and smarter equipment, the mining industry is experiencing Big Data, and in a different way to how the intelligence and marketing communities experience Big Data. Future mining will bring larger, more complicated and diverse data sets increasing the strain on traditional data integration and investigation techniques. Block caving is an important mining method for the extraction of massive, low-grade ore deposits due to its low operating cost and high production potential, given the right orebody geometry and ground conditions. This paper focuses on block caving due to its increasing popularity and importance in the future economic extraction of multiple commodities. The block cave mining system (BCMS) is a complex interrelation of factors, and due to the reliance on natural processes within the system, it is one of the least understood mining methods. Obtaining information on the factors within the BCMS generates large, complex data sets on various time scales. Each sensor network within the BCMS combines spatial and temporal information with tens to hundreds of recorded parameters. Data sets are quickly moving from multidimensional to n-dimensional. Optimisation of the BCMS is only possible through a holistic analysis approach, thus involving the integration of multiple, diverse, n-dimensional data sets (Big Data). This paper presents a current example BCMS Big Data situation involving 12 sources of information with differences in volume, data format and frequency of data generation. This suitability of virtual reality scientific visualisation (VRSV) as a solution to BCMS Big Data interpretation difficulties is then discussed. These 12 data sets can be effectively integrated using VRSV for intuitive and accelerated investigation of the BCMS. Potential future Big Data within the BCMS is presented involving 20 sources of information resulting from our increased need for monitoring and advancements in technology. Current methods of defining queries may not be suitable within these future complex, multidimensional data sets, and the potential of artificial intelligence algorithms within this system is discussed. These advancements in technology and machine learning lead to the requirement of live data acquisition and analysis. VRSV is identified as a suitable technology to satisfy the needs of real-time insight into the performance of these future caves for optimised management and reduced risk.
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