A comparative analysis of fragmentation selection algorithms for data warehouse partitioning

机译：数据仓库分区碎片选择算法的比较分析

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Enterprise data warehouse system maintains large amounts of data for enabling business analysis. The queries imposed on such system involves complex joins, aggregation and filter operations. Hence in order to enhance query performance the data warehouse needs to be tuned by optimization techniques such as partitioning. Referential horizontal partitioning performs better for data warehouse where the fact table is partitioned based on the dimension table. The number of fragments or partitions that is generated by horizontal partitioning might be very large to be managed in the underlying database. In literature few fragmentation selection algorithms have been proposed in order to choose optimal set of fragments. In this paper we provide a summary of different fragmentation selection algorithms and provide comparative analysis between them.

机译：企业数据仓库系统维护大量数据以进行业务分析。施加在此类系统上的查询涉及复杂的联接，聚合和筛选操作。因此，为了增强查询性能，需要通过诸如分区之类的优化技术来调整数据仓库。参照水平分区对于根据事实表对事实表进行分区的数据仓库而言，性能更好。水平分区生成的碎片或分区的数量可能非常大，无法在基础数据库中进行管理。在文献中，很少提出碎片选择算法以选择最佳的碎片集合。在本文中，我们总结了不同的碎片选择算法，并对它们进行了比较分析。

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来源
《2014 International Conference on Advances in Engineering and Technology Research》|2014年|1-5|共5页
会议地点 Unnao (IN)
作者
Thenmozhi M.; Vivekanandan K.;
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作者单位

Dept. of Comput. Sci. Eng., Pondicherry Eng. Coll., Pondicherry, India;

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原文格式 PDF
正文语种 eng
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关键词
data warehouses; feature selection; optimisation; business analysis; data warehouse partitioning; enterprise data warehouse system; fragmentation selection algorithm; optimization technique; Algorithm design and analysis; Data warehouses; Genetic algorithms; Genetics; Partitioning algorithms; Sociology; Statistics; Data Warehouse Partitioning; Fragmentation Selection; Horizontal Fragmentation; Referential Partitioning;

机译：数据仓库;功能选择;优化;业务分析;数据仓库分区;企业数据仓库系统;碎片选择算法;优化技术;算法设计和分析;数据仓库;遗传算法;遗传学;分区算法;社会学;统计数据;数据仓库分区;碎片选择;水平碎片;参照分区;;

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专利

1. Referential Horizontal Partitioning Selection Problem in Data Warehouses: Hardness Study and Selection Algorithms [J] . Bellatreche Ladjel, Boukhalfa Kamel, Richard Pascal, International Journal of Data Warehousing and Mining . 2009,第4期

机译：数据仓库中的参照水平分区选择问题：硬度研究和选择算法
2. Referential Horizontal Partitioning Selection Problem in Data Warehouses: Hardness Study and Selection Algorithms [J] . Ladjel Bellatreche, Kamel Boukhalfa, Pascal Richard, International Journal of Data Warehousing and Mining . 2009,第4期

机译：数据仓库中的参照水平分区选择问题：硬度研究和选择算法
3. A COMBINED ALGORITHM FOR DATA WAREHOUSE FRAGMENTATION SELECTION [J] . M.THENMOZHI, K.VIVEKANANDAN International Journal of Engineering Science and Technology . 2014,第5期

机译：数据仓库碎片选择的组合算法
4. A comparative analysis of fragmentation selection algorithms for data warehouse partitioning [C] . Thenmozhi M., Vivekanandan K. International Conference on Advances in Engineering and Technology Research . 2014

机译：数据仓库分区碎片选择算法的比较分析
5. Design and Simulation Analysis of Deep Learning Based Approaches and Multi-Attribute Algorithms for Warehouse Task Selection [D] . Sankaran, Prashant. 2020

机译：基于深度学习方法和多属性算法的仓库任务选择的设计与仿真分析
6. The Unsupervised Feature Selection Algorithms Based on Standard Deviation and Cosine Similarity for Genomic Data Analysis [O] . Juanying Xie, Mingzhao Wang, Shengquan Xu, 2021

机译：基于标准偏差和基因组数据分析的余弦相似性的无监督特征选择算法
7. Algorithms for Efficient Processing of Complex Queries in Node-Partitioned Data Warehouses [O] . 2008

机译：节点分区数据仓库中有效处理复杂查询的算法

A comparative analysis of fragmentation selection algorithms for data warehouse partitioning

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