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首页> 外文期刊>Journal of supercomputing >Static and incremental dynamic approaches for multi-objective bitmap join indexes selection in data warehouses
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Static and incremental dynamic approaches for multi-objective bitmap join indexes selection in data warehouses

机译：数据仓库中的多目标位图加入索引选择的静态和增量动态方法

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摘要

Data warehouses are very large databases and play key role in intelligent decision making in enterprises. The bitmap join indexes selection problem is crucial in the data warehouse physical design and known to be NP-hard. All the existing methods that solve this problem use single objective function and static query workload during the optimization. In the present work, we propose a multi-objective formulation of the problem using I) a static query workload and II) an incremental dynamic query workload. Three best well-known multi-objective evolutionary algorithms, Non-dominated sorting-based genetic algorithm II, S-Metric Selection Evolutionary Multi-Objective Algorithm and Strength Pareto Evolutionary Algorithm 2, are used to solve the multi-objective bitmap join indexes selection problem using both static and incremental dynamic query workloads. A set of experiments are performed to demonstrate the effectiveness of the proposed approaches. The incremental dynamic approach demonstrates a new perspective on bitmap join indexes optimization in a changing environment of an operational data warehouse.
机译：数据仓库是非常大的数据库，并在企业中智能决策中发挥关键作用。位图JOIN索引选择问题在数据仓库物理设计中至关重要，并且已知为NP-HARD。解决此问题的所有现有方法都使用单一目标函数和静态查询工作负载在优化期间。在本作工作中，我们提出了一种使用i）静态查询工作负载和II）的多目标配方使用静态查询工作负载。三种最着名的多目标进化算法，非主导分类的遗传算法II，S度量选择进化多目标算法和强度帕累托进化算法2，用于解决多目标位图加入索引选择问题使用静态和增量动态查询工作负载。进行一组实验以证明所提出的方法的有效性。增量动态方法演示了在操作数据仓库的更改环境中的位图Join索引优化的新透视图。

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