Data warehousing and mining technologies for adaptability in turbulent resources business environments

Historical resources data, geographically archived for decades, are source of analysing past business data dimensions and predicting their future turbulences, and periodic data are explored using data mining methodologies.

Abstract

Resources businesses often undergo turbulent and volatile periods, due to rapid increase of resource demand and poorly organised resources data volumes. This volatile industry operates multifaceted business units that manage heterogeneous data sources. Data integration and interactive business processes, distributed across complex business environments, need attention. Historical resources data, geographically (spatial dimension) archived for decades (periodic dimension), are source of analysing past business data dimensions and predicting their future turbulences. Periodic data, modelled in an integrated and robust warehouse environment, are explored using data mining methodologies. The data models presented, will optimise future inputs in the turbulent resources business environments.