Data warehousing and mining technologies for adaptability in turbulent resources business environments
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 businessproces...
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| Format: | Journal Article |
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Inderscience Enterprises Limited
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/10745 |
| _version_ | 1848747617126711296 |
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| author | Nimmagadda, Shastri Dreher, Heinz |
| author_facet | Nimmagadda, Shastri Dreher, Heinz |
| author_sort | Nimmagadda, Shastri |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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 businessprocesses, 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. |
| first_indexed | 2025-11-14T06:51:59Z |
| format | Journal Article |
| id | curtin-20.500.11937-10745 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:51:59Z |
| publishDate | 2011 |
| publisher | Inderscience Enterprises Limited |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-107452021-02-10T00:55:11Z Data warehousing and mining technologies for adaptability in turbulent resources business environments Nimmagadda, Shastri Dreher, Heinz resources business data data mining data warehousing 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 businessprocesses, 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. 2011 Journal Article http://hdl.handle.net/20.500.11937/10745 10.1504/IJBIDM.2011.039409 Inderscience Enterprises Limited fulltext |
| spellingShingle | resources business data data mining data warehousing Nimmagadda, Shastri Dreher, Heinz Data warehousing and mining technologies for adaptability in turbulent resources business environments |
| title | Data warehousing and mining technologies for adaptability in turbulent resources business environments |
| title_full | Data warehousing and mining technologies for adaptability in turbulent resources business environments |
| title_fullStr | Data warehousing and mining technologies for adaptability in turbulent resources business environments |
| title_full_unstemmed | Data warehousing and mining technologies for adaptability in turbulent resources business environments |
| title_short | Data warehousing and mining technologies for adaptability in turbulent resources business environments |
| title_sort | data warehousing and mining technologies for adaptability in turbulent resources business environments |
| topic | resources business data data mining data warehousing |
| url | http://hdl.handle.net/20.500.11937/10745 |