Information into Knowledge

Information on its own does not provide value since information must be used by knowledgeable domain experts (engineers or managers) to understand what to change be it an engineering design or workflow.  Innumerable tools and techniques are available to transform information into knowledge, from almost all disciplines.

OLAP cube design

Online Analytical Processing (OLAP) cubes are a relatively new technology which allows users to query (also called “slice and dice”) huge integrated data sets, driven from data warehouses.  OLAP querying allows users to query datasets that could be Terabytes in size nearly instantaneously, developing output that would take conventional querying hours or even days to display.  MIRG research has developed many extremely large and complex OLAP cubes using the Multi-dimensional Expressions (MDX).

Information Management

As data begins to be used at a highly detailed-level, one must typically better understand how the data is generated, and fix inefficiencies in both data collection and the business process itself.  MIRG researchers have developed in-house techniques to simultaneously model data and workflows, identify inefficiencies, then implement changes.

Knowledge Models

Many knowledge models have been developed in other sectors that require large amounts of easily accessible and clean data.  Being highly experience in the mining industry, MIRG researchers apply such models.  Examples of the techniques that have been employed include:

Numerical / Algorithms for Improved Process Outcomes.

  • Linear/goal/dynamic programming for optimizing mining processes
  • Empirical – example: block-cave draw control algorithms
  • Numerical – example: Lerch-grossman for finding optimal pit,
  • Analysis campaigns, the application of KDD (data mining)

Management - Benchmarking / Performance management

  • Six Sigma, Enterprise Asset Management, Balanced Scorecard, etc…
  • Process improvement
  • TQM, Reengineering, Business Process Redesign, Activity Based Costing, Just-in-time, Theory of Constraints, etc

Management - Decision Making

  • Simulation/what-if scenarios,
  • Activity Based Costing,
  • Real time optimal management through option simulation

Mine control, planning & holistic system optimization.

  • M2M, Holistic Dispatch, Fragmentation Optimization
  • Economic and planning decision models
  • Blend optimization through planning & control
  • Machine Intelligence
  • Preventative/predictive maintenance procedures
  • Information delivery, decision support feedback

You are looking at the Information to Knowledge process of the overall flow:

DataInformation