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    Roles of multidimensionality and granularity in warehousing Australian resources data

    33661.pdf (148.9Kb)
    Access Status
    Open access
    Authors
    Rudra, Amit
    Nimmagadda, Shastri
    Date
    2005
    Type
    Conference Paper
    
    Metadata
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    Citation
    Rudra, Amit and Nimmagadda, Shastri. 2005. : Roles of multidimensionality and granularity in warehousing Australian resources data, in Shafer, D.F. (ed), 38th Annual Hawaii International Conference on Systems Sciences 2005 (HICSS'05), Jan 03 2005, pp. 216-221. Hawaii: IEEE.
    Source Title
    Proceedings of the Thirty-Eigth Annual Hawaii International Conference on Systems Sciences 2005 (HICSS'05)
    Source Conference
    38th Annual Hawaii International Conference on Systems Sciences 2005 (HICSS'05)
    Faculty
    Curtin Business School
    School of Information Systems
    School
    Centre for Extended Enterprises and Business Intelligence
    Remarks

    Copyright 2005 IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

    URI
    http://hdl.handle.net/20.500.11937/33681
    Collection
    • Curtin Research Publications
    Abstract

    Granularity of data modeled in multidimensional data structures is an important factor for a data warehouse. Grain sizes and number of dimensions participating in the model are critical in ascertaining the quality of analytical queries that are run on such data warehouses. In this paper, exploration and production data of Australian resources industry, pertinent to oil and gas, over the past five decades have been examined for multidimensionality and grain size. This research shows how using an ER approach combined with multidimensional data modeling helps in considerable reduction in the size of the data warehouse, making it more effective and efficient.

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