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    On big data-guided upstream business research and its knowledge management

    Access Status
    Fulltext not available
    Authors
    Nimmagadda, Shastri
    Reiners, Torsten
    Wood, Lincoln
    Date
    2018
    Type
    Journal Article
    
    Metadata
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    Citation
    Nimmagadda, S. and Reiners, T. and Wood, L. 2018. On big data-guided upstream business research and its knowledge management. Journal of Business Research. 89: pp. 143-158.
    Source Title
    Journal of Business Research
    DOI
    10.1016/j.jbusres.2018.04.029
    ISSN
    0148-2963
    School
    School of Management
    URI
    http://hdl.handle.net/20.500.11937/67784
    Collection
    • Curtin Research Publications
    Abstract

    © 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.

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