On big data guided embedded digital ecosystems (EDE) and their knowledge management
In recent years, Big Data sources and their analytics have been the focus of many researchers on multiple ecosystems in both commercial and research organizations. The authors, currently focus on embedded ecosystems with Big Data motivation. The embedded systems hold large volumes and variety of het...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/51395 |
| _version_ | 1848758687731023872 |
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| author | Nimmagadda, Shastri Burke, Gary Reiners, Torsten |
| author_facet | Nimmagadda, Shastri Burke, Gary Reiners, Torsten |
| author_sort | Nimmagadda, Shastri |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In recent years, Big Data sources and their analytics have been the focus of many researchers on multiple ecosystems in both commercial and research organizations. The authors, currently focus on embedded ecosystems with Big Data motivation. The embedded systems hold large volumes and variety of heterogeneous, multidimensional data and their sources complicate the organization, accessibility, presentation and interpretation in producing and service companies. For example, the authors model various events associated with human-environment-economic ecosystems (HEEE) and exploit the impacts of human and environment ecosystems with respect to economic ecosystems. The objectives of the current research are to provide an understanding of the ecosystems and their inherent connectivity through integration of multiple ecosystems' Big Data sources using data warehousing and mining approaches. Domain ontologies are described for exploring the connectivity through an effective data integration process. To this extent, data patterns and trends hidden among Big Data sources of embedded ecosystems are analyzed for new domain knowledge and its interpretation. Data structures and implementation models deduced in the current work can guide ecosystems' researchers for forecasting of resources with a scope for developing information systems and their applications. Analyzing multiple domains and systems with robust methodologies facilitates the researchers to explore future alternatives and new opportunities of Big Data in the embedded ecosystems' research arena. |
| first_indexed | 2025-11-14T09:47:57Z |
| format | Conference Paper |
| id | curtin-20.500.11937-51395 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:47:57Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-513952017-09-13T15:47:18Z On big data guided embedded digital ecosystems (EDE) and their knowledge management Nimmagadda, Shastri Burke, Gary Reiners, Torsten In recent years, Big Data sources and their analytics have been the focus of many researchers on multiple ecosystems in both commercial and research organizations. The authors, currently focus on embedded ecosystems with Big Data motivation. The embedded systems hold large volumes and variety of heterogeneous, multidimensional data and their sources complicate the organization, accessibility, presentation and interpretation in producing and service companies. For example, the authors model various events associated with human-environment-economic ecosystems (HEEE) and exploit the impacts of human and environment ecosystems with respect to economic ecosystems. The objectives of the current research are to provide an understanding of the ecosystems and their inherent connectivity through integration of multiple ecosystems' Big Data sources using data warehousing and mining approaches. Domain ontologies are described for exploring the connectivity through an effective data integration process. To this extent, data patterns and trends hidden among Big Data sources of embedded ecosystems are analyzed for new domain knowledge and its interpretation. Data structures and implementation models deduced in the current work can guide ecosystems' researchers for forecasting of resources with a scope for developing information systems and their applications. Analyzing multiple domains and systems with robust methodologies facilitates the researchers to explore future alternatives and new opportunities of Big Data in the embedded ecosystems' research arena. 2017 Conference Paper http://hdl.handle.net/20.500.11937/51395 10.1109/INDIN.2016.7819370 restricted |
| spellingShingle | Nimmagadda, Shastri Burke, Gary Reiners, Torsten On big data guided embedded digital ecosystems (EDE) and their knowledge management |
| title | On big data guided embedded digital ecosystems (EDE) and their knowledge management |
| title_full | On big data guided embedded digital ecosystems (EDE) and their knowledge management |
| title_fullStr | On big data guided embedded digital ecosystems (EDE) and their knowledge management |
| title_full_unstemmed | On big data guided embedded digital ecosystems (EDE) and their knowledge management |
| title_short | On big data guided embedded digital ecosystems (EDE) and their knowledge management |
| title_sort | on big data guided embedded digital ecosystems (ede) and their knowledge management |
| url | http://hdl.handle.net/20.500.11937/51395 |