Abstract
In this paper we propose a comprehensive methodology for designing Parallel Relational Data Warehouses (PRDW) over database clusters, called \(\mathcal{F}\) ragmentation&\(\mathcal{A}\) llocation (\(\mathcal{F}\)&\(\mathcal{A}\)). \(\mathcal{F}\)&\(\mathcal{A}\) assumes that cluster nodes are heterogeneous in processing power and storage capacity, contrary to traditional design approaches that assume that cluster nodes are instead homogeneous, and fragmentation and allocation phases are performed in a simultaneous manner, contrary to traditional design approaches that instead perform these phases in an isolated manner. Also, a naive replication algorithm that takes into account the heterogeneous characteristics of our reference architecture is proposed. Finally, our proposal is experimentally assessed and validated against the widely-known data warehouse benchmark APB-1 release II.
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Bellatreche, L., Cuzzocrea, A., Benkrid, S. (2010). \(\mathcal{F}\)&\(\mathcal{A}\): A Methodology for Effectively and Efficiently Designing Parallel Relational Data Warehouses on Heterogenous Database Clusters. In: Bach Pedersen, T., Mohania, M.K., Tjoa, A.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2010. Lecture Notes in Computer Science, vol 6263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15105-7_8
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DOI: https://doi.org/10.1007/978-3-642-15105-7_8
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