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    Managing the Embedded Digital Ecosystems (EDE) Using Big Data Paradigm

    Access Status
    Fulltext not available
    Authors
    Nimmagadda, Shastri
    Rudra, Amit
    Date
    2017
    Type
    Book Chapter
    
    Metadata
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    Citation
    Nimmagadda, S. and Rudra, A. 2017. Managing the Embedded Digital Ecosystems (EDE) Using Big Data Paradigm, in Ben Kei Daniel (ed), Big Data and Learning Analytics in Higher Education. Cham, IL: Springer.
    Source Title
    Big Data and Learning Analytics in Higher Education
    DOI
    10.1007/978-3-319-06520-5_5
    ISBN
    978-3-319-06520-5
    School
    School of Information Systems
    URI
    http://hdl.handle.net/20.500.11937/6818
    Collection
    • Curtin Research Publications
    Abstract

    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.

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