Big-data Integration Methodologies for effective management and data mining of petroleum digital ecosystems
Petroleum industries' big data characterize heterogeneity and they are often multidimensional in nature. In the recent past, explorers narrate petroleum system, as an ecosystem, in which elements and processes are constantly interacted and communicated each other. Exploration is one of the key...
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| Format: | Conference Paper |
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IEEE
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/24214 |
| _version_ | 1848751367167934464 |
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| author | Nimmagadda, Shastri Dreher, Heinz |
| author2 | Fulvio Frati |
| author_facet | Fulvio Frati Nimmagadda, Shastri Dreher, Heinz |
| author_sort | Nimmagadda, Shastri |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Petroleum industries' big data characterize heterogeneity and they are often multidimensional in nature. In the recent past, explorers narrate petroleum system, as an ecosystem, in which elements and processes are constantly interacted and communicated each other. Exploration is one of the key super-type data dimensions of petroleum ecosystem, (including seismic dimension), exhibiting high degree of heterogeneity, sequence identity and structural similarity; this is especially the case for, elements and processes that are unique to petroleum systems of South East Asia. Existing approaches of petroleum data organizations have limitations in capturing and integrating petroleum systems data. An alternative method uses ontologies and does not rely on keywords or similarity metrics. The conceptual framework of petroleum ontology (PO) is to promote reuse of concepts and a set of algebraic operators for querying petroleum ontology instances. This ontology-based fine-grained multidimensional data structuring adapts to warehouse metadata modeling. The data integration process facilitates to metadata models, which are deduced for Indonesian sedimentary basins, and is useful for data mining and subsequent data interpretation including geological knowledge mapping. |
| first_indexed | 2025-11-14T07:51:36Z |
| format | Conference Paper |
| id | curtin-20.500.11937-24214 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:51:36Z |
| publishDate | 2013 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-242142017-09-13T15:08:24Z Big-data Integration Methodologies for effective management and data mining of petroleum digital ecosystems Nimmagadda, Shastri Dreher, Heinz Fulvio Frati data mining data fusion ontologies data integration petroleum bearing sedimentary basin Data warehousing Petroleum industries' big data characterize heterogeneity and they are often multidimensional in nature. In the recent past, explorers narrate petroleum system, as an ecosystem, in which elements and processes are constantly interacted and communicated each other. Exploration is one of the key super-type data dimensions of petroleum ecosystem, (including seismic dimension), exhibiting high degree of heterogeneity, sequence identity and structural similarity; this is especially the case for, elements and processes that are unique to petroleum systems of South East Asia. Existing approaches of petroleum data organizations have limitations in capturing and integrating petroleum systems data. An alternative method uses ontologies and does not rely on keywords or similarity metrics. The conceptual framework of petroleum ontology (PO) is to promote reuse of concepts and a set of algebraic operators for querying petroleum ontology instances. This ontology-based fine-grained multidimensional data structuring adapts to warehouse metadata modeling. The data integration process facilitates to metadata models, which are deduced for Indonesian sedimentary basins, and is useful for data mining and subsequent data interpretation including geological knowledge mapping. 2013 Conference Paper http://hdl.handle.net/20.500.11937/24214 10.1109/DEST.2013.6611345 IEEE restricted |
| spellingShingle | data mining data fusion ontologies data integration petroleum bearing sedimentary basin Data warehousing Nimmagadda, Shastri Dreher, Heinz Big-data Integration Methodologies for effective management and data mining of petroleum digital ecosystems |
| title | Big-data Integration Methodologies for effective management and data mining of petroleum digital ecosystems |
| title_full | Big-data Integration Methodologies for effective management and data mining of petroleum digital ecosystems |
| title_fullStr | Big-data Integration Methodologies for effective management and data mining of petroleum digital ecosystems |
| title_full_unstemmed | Big-data Integration Methodologies for effective management and data mining of petroleum digital ecosystems |
| title_short | Big-data Integration Methodologies for effective management and data mining of petroleum digital ecosystems |
| title_sort | big-data integration methodologies for effective management and data mining of petroleum digital ecosystems |
| topic | data mining data fusion ontologies data integration petroleum bearing sedimentary basin Data warehousing |
| url | http://hdl.handle.net/20.500.11937/24214 |