Abstract
Parallel data platforms are recognized as a key solution for processing analytical queries running on extremely large data warehouses (DWs). Deploying a DW on such platforms requires efficient data partitioning and allocation techniques. Most of these techniques assume a priori knowledge of workload. To deal with their evolution, reactive strategies are mainly used. The BI 2.0 requirements have put large batch and ad-hoc user queries at the center. Consequently, reactive-based solutions for deploying a DW in parallel platforms are not sufficient. Autonomous computing has emerged as a paradigm that allows digital objects managing themselves in accordance with high-level guidance by the means of proactive approaches. Being inspired by this paradigm, we propose in this paper, a proactive approach based on a query clustering model to deploying a DW over a parallel platform. The query clustering triggers partitioning and allocation processes by considering only evolved query groups. Intensive experiments were conducted to show the efficiency of our proposal.
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Benkrid, S., Bellatreche, L. (2020). A Framework for Designing Autonomous Parallel Data Warehouses. In: Wen, S., Zomaya, A., Yang, L.T. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2019. Lecture Notes in Computer Science(), vol 11945. Springer, Cham. https://doi.org/10.1007/978-3-030-38961-1_9
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DOI: https://doi.org/10.1007/978-3-030-38961-1_9
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