Abstract
With the big data era and the cloud, several applications are designed around analytical aspects, where the data warehousing technology is in the heart of their construction chain. The interaction between queries in such environments represents a big challenge due to three dimensions: (i) the routinely aspects of queries, (ii) their large number, and (iii) the high operation sharing between queries. In the context of very large databases, these operations are expensive and need to be optimized. The horizontal data partitioning (\(\mathcal{HDP}\)) is a pre-condition for designing extremely large databases in several environments: centralized, distributed, parallel and cloud. It aims to reduce the cost of these operations. In \(\mathcal{HDP}\), the optimization space of potential candidates for partitioning grows exponentially with the problem size making the problem NP-hard. In this paper, we propose a new approach based on query interactions to select a partitioning schema of a data warehouse in a divide and conquer manner to achieve an improved trade-off between the optimization algorithm’s speed and the quality of the solution. The effectiveness of our approach is proven through a validation using the Star Schema Benchmark (100 GB) on Oracle11g.
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Bellatreche, L., Kerkad, A., Breß, S., Geniet, D. (2013). RouPar: Routinely and Mixed Query-Driven Approach for Data Partitioning. In: Meersman, R., et al. On the Move to Meaningful Internet Systems: OTM 2013 Conferences. OTM 2013. Lecture Notes in Computer Science, vol 8185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41030-7_23
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DOI: https://doi.org/10.1007/978-3-642-41030-7_23
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