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Big Mines, Bigger Data

Big data- everyone seems to be talking about it, but what is it really? More importantly, how will big data change the way we mine? Where is it from, how do we process it and what happens to the results?

What is big data?

Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capturing, data curation, searching, sharing, storage, transfer, visualization, querying and information privacy. Doug Laney (2001) defined big data as three-dimensional, increasing volume (amount of data), velocity (speed of data in and out) and variety (range of data types and sources). Successfully harnessing big data typically requires various physical and/or virtual machines working together in an integrated manner to manage and process the data in an acceptable amount of time.

Mining’s big (data) problem:

There are many different professionals creating and interacting with mining data during the lifecycle of a mine. Each of them uses systems designed to solve their unique technical challenges. Getting these systems to effectively share information is a seemingly impossible challenge.

All the data you need to mine by, is already being created today, it is just not being related correctly. Stored in individual systems and files, everything is all over the place. This means that asking multi-disciplinary questions such as compliance to original planning becomes a huge ask. The data for this answer does not exist in a single context – but it does exist!

This implies the need to look at (aboriginal) data amalgamation instead of merely integrating systems. Every bit of data created in stand-alone applications needs to be stored in an amalgamated database. Unpacking the aboriginal application’s data through bi-directional connectors and convertors, and amalgamating that data centrally, means that you can re-create the original file again. Previously fragmented data can now be related to other data sets through its mutual relation to space.

For example, relating mine design information with the lease information can be done through the relation of both datasets with the physical space they share. They key to this spatial integration paradigm lies in MineRP’s approach to creating the context in which you manage and relate mining data.

Get a handle on your (big) mining data:

All mining data, regardless of your mining technical system, vendor or profession have one thing in common: its relation to space. Mining data is built around space, yet every application has its own coordinate grid - some being proprietary, some local mine based and everything stored in its own instance of space. By standardizing on a single version of space through a centrally managed spatial database, we can pull all these applications onto one platform. This creates an opportunity for cross-discipline questions with pictures as answers.

MineRP effectively amalgamates fragmented mining data and changes the way data is stored, without requiring of our clients to change the tools used to create the data in the first place. Practically, there may not be huge volumes of data in mining, but the variety of data sources, and the velocity at which data changes in one dataset impacts the whole, introducing unique big data challenges. If something small changes, everything changes – because everything is spatially related. Because MineRP amalgamates the data, we now have a unique solution to addressing mining’s big data problems.

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