Initial Optimization Techniques for the Cube Algebra Query Language: The Relational Model as a Target

IF 0.5 4区计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING

International Journal of Data Warehousing and Mining Pub Date : 2022-01-01 DOI:10.4018/ijdwm.299016

Thomas Mercieca, J. Vella, K. Vella

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Abstract

A common model used in addressing today's overwhelming amounts of data is the OLAP Cube. The OLAP community has proposed several cube algebras, although a standard has still not been nominated. This study focuses on a recent addition to the cube algebras: the user-centric Cube Algebra Query Language (CAQL). The study aims to explore the optimization potential of this algebra by applying logical rewriting inspired by classic relational algebra and parallelism. The lack of standard algebra is often cited as a problem in such discussions. Thus, the significance of this work is that of strengthening the position of this algebra within the OLAP algebras by addressing implementation details. The modern open-source PostgreSQL relational engine is used to encode the CAQL abstraction. A query workload based on a well-known dataset is adopted, and CAQL and SQL implementations are compared. Finally, the quality of the query created is evaluated through the observed performance characteristics of the query. Results show strong improvements over the baseline case of the unoptimized query.

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多维代数查询语言的初始优化技术:以关系模型为目标

用于处理当今海量数据的一个常用模型是OLAP Cube。OLAP社区已经提出了几个立方体代数，尽管还没有一个标准被提名。本研究主要关注立方体代数的新成员:以用户为中心的立方体代数查询语言(CAQL)。本研究旨在利用经典关系代数的逻辑改写和并行性来探索该代数的优化潜力。在这样的讨论中，缺乏标准代数经常被引用为一个问题。因此，这项工作的意义在于通过解决实现细节来加强该代数在OLAP代数中的地位。使用现代开源的PostgreSQL关系引擎对CAQL抽象进行编码。采用基于知名数据集的查询工作负载，对CAQL和SQL实现进行了比较。最后，通过观察查询的性能特征来评估所创建查询的质量。结果显示，与未优化查询的基线情况相比，有很大的改进。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Data Warehousing and Mining COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

2.40

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The International Journal of Data Warehousing and Mining (IJDWM) disseminates the latest international research findings in the areas of data management and analyzation. IJDWM provides a forum for state-of-the-art developments and research, as well as current innovative activities focusing on the integration between the fields of data warehousing and data mining. Emphasizing applicability to real world problems, this journal meets the needs of both academic researchers and practicing IT professionals.The journal is devoted to the publications of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. The journal accepts paper submission of any work relevant to data warehousing and data mining. Special attention will be given to papers focusing on mining of data from data warehouses; integration of databases, data warehousing, and data mining; and holistic approaches to mining and archiving