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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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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