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首页> 外文期刊>Journal of database management >Effectively and Efficiently Designing and Querying Parallel Relational Data Warehouses on Heterogeneous Database Clusters: The F&A Approach
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Effectively and Efficiently Designing and Querying Parallel Relational Data Warehouses on Heterogeneous Database Clusters: The F&A Approach

机译：在异构数据库集群上有效，高效地设计和查询并行关系数据仓库：F＆A方法

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In this paper, a comprehensive methodology for designing and querying Parallel Rational Data Warehouses (PRDW) over database clusters, called Fragmentation & Allocation (F&A) is proposed. F&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. In classical approaches, two different cost models are used to perform fragmentation and allocation, separately, whereas F&A makes use of one cost model that considers fragmentation and allocation parameters simultaneously. Therefore, according to the F&A methodology proposed, the allocation phase/decision is done at fragmentation. At the fragmentation phase, F&A uses two well-known algorithms, namely Hill Climbing (HC) and Genetic Algorithm (GA), which the authors adapt to the main PRDW design problem over heterogeneous database clusters, as these algorithms are capable of taking into account the heterogeneous characteristics of the reference application scenario. At the allocation phase, F&A introduces an innovative matrix-based formalism capable of capturing the interactions among fragments, input queries, and cluster node characteristics, driving the data allocation task accordingly, and a related affinity-based algorithm, called F&A-ALLOC. Finally, their proposal is experimentally assessed and validated against the widely-known data warehouse benchmark APB-1 release Ⅱ.
机译：本文提出了一种用于设计和查询数据库集群上的并行Rational Data Warehouse（PRDW）的综合方法，称为分片与分配（F＆A）。 F＆A假定群集节点在处理能力和存储容量上是异构的，这与传统的设计方法相反，传统的设计方法假定群集节点是同质的，并且分段和分配阶段以同时方式执行。在经典方法中，两个不同的成本模型分别用于执行分段和分配，而F＆A使用一个同时考虑分段和分配参数的成本模型。因此，根据所提出的F＆A方法，分配阶段/决策是在分散的情况下完成的。在碎片化阶段，F＆A使用两种众所周知的算法，即“爬山”（HC）和“遗传算法”（GA），作者将其适应异构数据库集群上的主要PRDW设计问题，因为这些算法能够考虑到参考应用场景的异构特性。在分配阶段，F＆A引入了一种创新的基于矩阵的形式主义，这种形式主义能够捕获片段，输入查询和集群节点特征之间的交互，从而驱动数据分配任务，以及一种相关的基于亲和力的算法，称为F＆A-ALLOC。最后，他们的建议是根据广为人知的数据仓库基准APB-1版本Ⅱ进行实验评估和验证的。

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