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Bi-Objective Optimization Method for Horizontal Fragmentation Problem in Relational Data Warehouses as a Linear Programming Problem

机译:关系数据仓库中水平碎片问题的双目标优化方法作为线性规划问题

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摘要

In this work, we relied on a particular exact method to solve NP-Hard problem of determining a horizontal fragmentation scheme in relational data warehouses. The method used is that of linear programming which is distinguished by other methods by the existence of practical methods that facilitate the resolution of problems that may be described in linear form. We quote the Simplex method and the interior points. To meet the linearity of the objective function and constraints, we used initially "De Morgan" theorem, which is based on properties of sets to transform and optimize decision queries, from any form to a linear one.In addition to designing and solving the selection problem of horizontal fragmentation technique, we considered the problem in two simultaneous objectives, namely: the number of Inputs/Outputs needed to run the global workload, and number of fragments generated to identify the best solutions compared to the concept of Pareto dominance.In addition, to carry out our work, we used the Benchmark APB1 invoked by a workload, to achieve satisfactory results.
机译:在这项工作中,我们依靠一种特定的精确方法来解决在关系数据仓库中确定水平碎片方案的NP-Hard问题。所使用的方法是线性编程的方法,其与其他方法的区别在于存在实用的方法,这些方法有助于解决可能以线性形式描述的问题。我们引用单纯形法和内部要点。为了满足目标函数和约束的线性,我们最初使用“ De Morgan”定理,该定理基于集合的属性来转换和优化从任何形式到线性形式的决策查询。除了设计和求解之外对于水平分段技术的选择问题,我们同时考虑了两个目标,即:运行全局工作负载所需的输入/输出数量,以及与帕累托优势概念相比为识别最佳解决方案而生成的片段数量。此外,为了执行我们的工作,我们使用了工作负载所调用的Benchmark APB1,以获得令人满意的结果。

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