| Summary: | Historical Australian resources (exploration and production) data are stored in data warehouse environment in the form of relational and hierarchical data structures in multiple dimensions. Significantly, these resources databases consist of periodic dimension, characterizing the role of period and its relation among other data dimensions, their attributes and fact tables. Data mining of periodic data instances in resources industry is an emerging discipline that can map business knowledge from variety of very large databases. Several materialized data views are accessed from the resources data warehouse using various data mining procedures for discovering data, links, associations and patterns; interpretation of these patterns (such as periodicity, seasonality, or cycles) that led to predictions for future business forecast. Mining models generated among multiple dimensions, will facilitate managers of decision support personnel for making future predictions. This present study extracts business intelligence from historical data, which is presented in terms of data visualization, an approach of business knowledge representation and interpretation.
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