Multivariate data analysis with readings (2nd ed.)
Multivariate data analysis with readings (2nd ed.)
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Data Warehousing: Strategies, Technologies, and Techniques
Data Warehousing: Strategies, Technologies, and Techniques
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Using ontology to validate conceptual models
Communications of the ACM - Service-oriented computing
A Distributed Approach to Sub-Ontology Extraction
AINA '04 Proceedings of the 18th International Conference on Advanced Information Networking and Applications - Volume 2
Modern Database Management (7th Edition)
Modern Database Management (7th Edition)
Roles of Multidimensionality and Granularity in Warehousing Australian Resources Data
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences - Volume 08
Design of petroleum company's metadata and an effective knowledge mapping methodology
ISC '07 Proceedings of the 10th IASTED International Conference on Intelligent Systems and Control
A distributed ontology framework for the grid
PDCAT'04 Proceedings of the 5th international conference on Parallel and Distributed Computing: applications and Technologies
On new emerging concepts of petroleum digital ecosystem
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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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.