Managing the Embedded Digital Ecosystems (EDE) Using Big Data Paradigm
Big data sources and their mining from multitude of ecosystems have been the focus of many researchers in both commercial and research organizations. The authors in the current research have focused on embedded ecosystems with big data motivation. Embedded systems hold volumes and a variety of heter...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Book Chapter |
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Springer
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/6818 |
| _version_ | 1848745186458337280 |
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| author | Nimmagadda, Shastri Rudra, Amit |
| author2 | Ben Kei Daniel |
| author_facet | Ben Kei Daniel Nimmagadda, Shastri Rudra, Amit |
| author_sort | Nimmagadda, Shastri |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Big data sources and their mining from multitude of ecosystems have been the focus of many researchers in both commercial and research organizations. The authors in the current research have focused on embedded ecosystems with big data motivation. Embedded systems hold volumes and a variety of heterogeneous, multidimensional data, and their sources complicate their organization, accessibility, presentation, and interpretation. Objectives of the current research are to provide improved understanding of ecosystems and their inherent connectivity by integrating multiple ecosystems’ big data sources in a data warehouse environment and their analysis with multivariate attribute instances and magnitudes. Domain ontologies are described for connectivity, effective data integration, and mining of embedded ecosystems. The authors attempt to exploit the impacts of disease and environment ecosystems on human ecosystems. To this extent, data patterns, trends, and correlations hidden among big data sources of embedded ecosystems are analyzed for domain knowledge. Data structures and implementation models deduced in the current work can guide the researchers of health care, welfare, and environment for forecasting of resources and managing information systems that involve with big data. Analyzing embedded ecosystems with robust methodologies facilitates the researchers to explore scope and new opportunities in the domain research. |
| first_indexed | 2025-11-14T06:13:21Z |
| format | Book Chapter |
| id | curtin-20.500.11937-6818 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:13:21Z |
| publishDate | 2017 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-68182023-02-27T07:34:28Z Managing the Embedded Digital Ecosystems (EDE) Using Big Data Paradigm Nimmagadda, Shastri Rudra, Amit Ben Kei Daniel Russell Butson Big data sources and their mining from multitude of ecosystems have been the focus of many researchers in both commercial and research organizations. The authors in the current research have focused on embedded ecosystems with big data motivation. Embedded systems hold volumes and a variety of heterogeneous, multidimensional data, and their sources complicate their organization, accessibility, presentation, and interpretation. Objectives of the current research are to provide improved understanding of ecosystems and their inherent connectivity by integrating multiple ecosystems’ big data sources in a data warehouse environment and their analysis with multivariate attribute instances and magnitudes. Domain ontologies are described for connectivity, effective data integration, and mining of embedded ecosystems. The authors attempt to exploit the impacts of disease and environment ecosystems on human ecosystems. To this extent, data patterns, trends, and correlations hidden among big data sources of embedded ecosystems are analyzed for domain knowledge. Data structures and implementation models deduced in the current work can guide the researchers of health care, welfare, and environment for forecasting of resources and managing information systems that involve with big data. Analyzing embedded ecosystems with robust methodologies facilitates the researchers to explore scope and new opportunities in the domain research. 2017 Book Chapter http://hdl.handle.net/20.500.11937/6818 10.1007/978-3-319-06520-5_5 Springer restricted |
| spellingShingle | Nimmagadda, Shastri Rudra, Amit Managing the Embedded Digital Ecosystems (EDE) Using Big Data Paradigm |
| title | Managing the Embedded Digital Ecosystems (EDE) Using Big Data Paradigm |
| title_full | Managing the Embedded Digital Ecosystems (EDE) Using Big Data Paradigm |
| title_fullStr | Managing the Embedded Digital Ecosystems (EDE) Using Big Data Paradigm |
| title_full_unstemmed | Managing the Embedded Digital Ecosystems (EDE) Using Big Data Paradigm |
| title_short | Managing the Embedded Digital Ecosystems (EDE) Using Big Data Paradigm |
| title_sort | managing the embedded digital ecosystems (ede) using big data paradigm |
| url | http://hdl.handle.net/20.500.11937/6818 |