Roles of multidimensionality and granularity in warehousing Australian resources data

Granularity of data modeled in multidimensional data structures is an important factor for a data warehouse. Grain sizes and number of dimensions participating in the model are critical in ascertaining the quality of analytical queries that are run on such data warehouses. In this paper, exploration and production data of Australian resources industry, pertinent to oil and gas, over the past five decades have been examined for multidimensionality and grain size. This research shows how using an ER approach combined with multidimensional data modeling helps in considerable reduction in the size of the data warehouse, making it more effective and efficient

Big Data Guided Resources Businesses – Leveraging Location Analytics and Managing Geospatial-temporal Knowledge

Location data rapidly grow with fast-changing logistics and business rules. Due to fast-growing business ventures and their diverse operations locally and globally, location-based information systems are in demand in resource industries. Data sources in these industries are spatial-temporal, with petabytes in size. Managing volumes and various data in periodic and geographic dimensions using the existing modelling methods is challenging. The current relational database models have implementation challenges, including the interpretation of data views. Multidimensional models are articulated to integrate resource databases with spatial-temporal attribute dimensions. Location and periodic attribute dimensions are incorporated into various schemas to minimise ambiguity during database operations, ensuring resource data's uniqueness and monotonic characteristics. We develop an integrated framework compatible with the multidimensional repository and implement its metadata in resource industries. The resources’ metadata with spatial-temporal attributes enables business research analysts a scope for data views’ interpretation in new geospatial knowledge domains for financial decision support