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, exploratio...
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| Format: | Conference Paper |
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IEEE
2005
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| Online Access: | http://hdl.handle.net/20.500.11937/33681 |
| _version_ | 1848754014706991104 |
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| author | Rudra, Amit Nimmagadda, Shastri |
| author_facet | Rudra, Amit Nimmagadda, Shastri |
| author_sort | Rudra, Amit |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T08:33:41Z |
| format | Conference Paper |
| id | curtin-20.500.11937-33681 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:33:41Z |
| publishDate | 2005 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-336812020-10-05T01:06:52Z Roles of multidimensionality and granularity in warehousing Australian resources data Rudra, Amit Nimmagadda, Shastri grain size data modeling data warehouse warehousing ER granularity resources data Australia resources multidimensionality and granularity Australia 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. 2005 Conference Paper http://hdl.handle.net/20.500.11937/33681 IEEE fulltext |
| spellingShingle | grain size data modeling data warehouse warehousing ER granularity resources data Australia resources multidimensionality and granularity Australia Rudra, Amit Nimmagadda, Shastri Roles of multidimensionality and granularity in warehousing Australian resources data |
| title | Roles of multidimensionality and granularity in warehousing Australian resources data |
| title_full | Roles of multidimensionality and granularity in warehousing Australian resources data |
| title_fullStr | Roles of multidimensionality and granularity in warehousing Australian resources data |
| title_full_unstemmed | Roles of multidimensionality and granularity in warehousing Australian resources data |
| title_short | Roles of multidimensionality and granularity in warehousing Australian resources data |
| title_sort | roles of multidimensionality and granularity in warehousing australian resources data |
| topic | grain size data modeling data warehouse warehousing ER granularity resources data Australia resources multidimensionality and granularity Australia |
| url | http://hdl.handle.net/20.500.11937/33681 |