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ABSTRACT
A myriad of spatial and attribute data are in use when designing and operating mine-engineering systems. These data are hosted on various servers of a company and are diverse in structure of data and units of measurement and have different subject areas related to each other in a complex way. There is no conformity between conventional systems for managing and storing data and tools for analysing incoming data. Big Data paradigm that implies methods for processing distributed data is utilised in ‘big data’ processing. A MapReduce method involves two procedures: map procedure that applies a proper function to each element of the list and reduce procedure, integrating the map procedure results. Such conventional database management system methods as integration and indexation, graph search and other methods are used to cluster big data. These methods should be used within MapReduce. Employing standard equipment and means to control a distributed Hadoop and Apache Hadoop Distributed File System (HDFS) file system, data storages in petabytes can be implemented. To make a more in-depth analysis, Data Mining methods are utilised along with network analysis, predictive analytics, etc. The articlecovers various methods applied to collect and analyse ‘big data’ to do a feasibility study and design mine and engineering systems.