On big data-guided upstream business research and its knowledge management
© 2018 The emerging Big Data integration imposes diverse challenges, compromising the sustainable business research practice. Heterogeneity, multi-dimensionality, velocity, and massive volumes that challenge Big Data paradigm may preclude the effective data and system integration processes. Business...
| Main Authors: | , , |
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| Format: | Journal Article |
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Elsevier
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/67784 |
| _version_ | 1848761657273090048 |
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| author | Nimmagadda, Shastri Reiners, Torsten Wood, Lincoln |
| author_facet | Nimmagadda, Shastri Reiners, Torsten Wood, Lincoln |
| author_sort | Nimmagadda, Shastri |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2018 The emerging Big Data integration imposes diverse challenges, compromising the sustainable business research practice. Heterogeneity, multi-dimensionality, velocity, and massive volumes that challenge Big Data paradigm may preclude the effective data and system integration processes. Business alignments get affected within and across joint ventures as enterprises attempt to adapt to changes in industrial environments rapidly. In the context of the Oil and Gas industry, we design integrated artefacts for a resilient multidimensional warehouse repository. With access to several decades of resource data in upstream companies, we incorporate knowledge-based data models with spatial-temporal dimensions in data schemas to minimize ambiguity in warehouse repository implementation. The design considerations ensure uniqueness and monotonic properties of dimensions, maintaining the connectivity between artefacts and achieving the business alignments. The multidimensional attributes envisage Big Data analysts a scope of business research with valuable new knowledge for decision support systems and adding further business values in geographic scales. |
| first_indexed | 2025-11-14T10:35:09Z |
| format | Journal Article |
| id | curtin-20.500.11937-67784 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:35:09Z |
| publishDate | 2018 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-677842018-05-18T08:05:01Z On big data-guided upstream business research and its knowledge management Nimmagadda, Shastri Reiners, Torsten Wood, Lincoln © 2018 The emerging Big Data integration imposes diverse challenges, compromising the sustainable business research practice. Heterogeneity, multi-dimensionality, velocity, and massive volumes that challenge Big Data paradigm may preclude the effective data and system integration processes. Business alignments get affected within and across joint ventures as enterprises attempt to adapt to changes in industrial environments rapidly. In the context of the Oil and Gas industry, we design integrated artefacts for a resilient multidimensional warehouse repository. With access to several decades of resource data in upstream companies, we incorporate knowledge-based data models with spatial-temporal dimensions in data schemas to minimize ambiguity in warehouse repository implementation. The design considerations ensure uniqueness and monotonic properties of dimensions, maintaining the connectivity between artefacts and achieving the business alignments. The multidimensional attributes envisage Big Data analysts a scope of business research with valuable new knowledge for decision support systems and adding further business values in geographic scales. 2018 Journal Article http://hdl.handle.net/20.500.11937/67784 10.1016/j.jbusres.2018.04.029 Elsevier restricted |
| spellingShingle | Nimmagadda, Shastri Reiners, Torsten Wood, Lincoln On big data-guided upstream business research and its knowledge management |
| title | On big data-guided upstream business research and its knowledge management |
| title_full | On big data-guided upstream business research and its knowledge management |
| title_fullStr | On big data-guided upstream business research and its knowledge management |
| title_full_unstemmed | On big data-guided upstream business research and its knowledge management |
| title_short | On big data-guided upstream business research and its knowledge management |
| title_sort | on big data-guided upstream business research and its knowledge management |
| url | http://hdl.handle.net/20.500.11937/67784 |