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Evaluation of Data Warehouse Design Methodologies in the Context of Big Data

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Big Data Analytics and Knowledge Discovery (DaWaK 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10440))

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Abstract

The data warehouse design methodologies require a novel approach in the Big Data context, because the methodologies have to provide solutions to face the issues related to the 5 Vs (Volume, Velocity, Variety, Veracity, and Value). So it is mandatory to support the designer through automatic techniques able to quickly produce a multidimensional schema using and integrating several data sources, which can be also unstructured and, therefore, need an ontology-based reasoning. Accordingly, the methodologies have to adopt agile techniques, in order to change the multidimensional schema as the business requirements change, without a complete design process. Furthermore, hybrid approaches must be used instead of the traditional data-driven or requirement-driven approaches, in order to avoid missing the adhesion to user requirements and to produce a valuable multidimensional schema compliant with data sources. In the paper, we perform a metric comparison among different methodologies, in order to demonstrate that methodologies classified as hybrid, ontology-based, automatic, and agile are tailored for the Big Data context.

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Notes

  1. 1.

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Authors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Bari Aldo Moro, via Orabona 4, 70125, Bari, Italy

    Francesco Di Tria, Ezio Lefons & Filippo Tangorra

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  1. Francesco Di Tria
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  2. Ezio Lefons
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  3. Filippo Tangorra
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Correspondence to Francesco Di Tria .

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Editors and Affiliations

  1. LIAS/ISAE-ENSMA, Chasseneuil, France

    Ladjel Bellatreche

  2. University of Texas at Arlington, Arlington, Texas, USA

    Sharma Chakravarthy

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Di Tria, F., Lefons, E., Tangorra, F. (2017). Evaluation of Data Warehouse Design Methodologies in the Context of Big Data. In: Bellatreche, L., Chakravarthy, S. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2017. Lecture Notes in Computer Science(), vol 10440. Springer, Cham. https://doi.org/10.1007/978-3-319-64283-3_1

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  • DOI: https://doi.org/10.1007/978-3-319-64283-3_1

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