On Digital Opencast Mining Ecosystems (DOME) and Knowledge Management – a Big Data Perspective

Abstract

Many opencast mines inhabit thousands of square km area, which are productive and commercial Australia wide. Hundreds of volumes and varieties of data dimensions and facts exist in the opencast mining areas. The data sources linked with various opencast mines are often heterogeneous and multidimensional. Data modelling is challenging in a Big Data scale, at times precluding the data integration process. The mineralization connected to opencast mines occurs in shafts, pit slopes, ramps and benches with varying geometries and configurations in large-scale geographic and periodic dimensions. The limits of the mineralization at places are either unknown and or ambiguously interpreted. The Big Data, in the context of the Australian mining industry, are due to the explosive growth of data sources and their uncontrolled management in many national and multinational companies. New knowledge is required for interpreting new opencast mining areas and their mineralization. For sustainable production, the knowledge of the connectivity between mineralization and its associated opencast mines is constrained. We propose an empirical modelling, analysing hundreds of attribute dimensions and fact instances of geological and geophysical vintages in the mining areas. Different data constructs and models are built for logical metadata, accommodating it in a multidimensional warehouse repository, as a DOME solution. It is an innovative solution to the mining industry's Big Data problem including the opencast mine planning and design, adding values to the existing domain knowledge with new interpretations. Various geological events attributed to the interpretation and distribution of mineralization are useful for the opencast mine managers.