使用非同义物化查询的数据仓库新方法

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
S. Chakraborty
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引用次数: 1

摘要

来自多个源的数据被加载到组织数据仓库中进行分析。由于一些OLAP查询经常在仓库数据上触发,因此通过将查询和结果存储在关系数据库(称为物化查询数据库(MQDB))中,可以减少它们的执行时间。如果输入查询和存储查询的表、字段、函数和条件相同,但WHERE或HAVING子句中指定的查询条件不匹配,则认为它们彼此非同义。在本研究中,非同义查询的结果是通过对已有存储的结果进行UNION或MINUS操作后重用产生的。这将减少非同义查询的执行时间。对于输入查询的超集标准值,应用UNION操作,对于子集值,应用MINUS操作。如果需要,可以使用Data markets对现有存储结果进行增量结果处理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Approach Using Non-Synonymous Materialized Queries for Data Warehousing
Data from multiple sources are loaded into the organization data warehouse for analysis. Since some OLAP queries are quite frequently fired on the warehouse data, their execution time is reduced by storing the queries and results in a relational database, referred as materialized query database (MQDB). If the tables, fields, functions, and criteria of input query and stored query are the same but the query criteria specified in WHERE or HAVING clause do not match, then they are considered non-synonymous to each other. In the present research, the results of non-synonymous queries are generated by reusing the existing stored results after applying UNION or MINUS operations on them. This will reduce the execution time of non-synonymous queries. For superset criteria values of input query, UNION operation is applied, and for subset values, MINUS operation is applied. Incremental result processing of existing stored results, if required, is performed using Data Marts.
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来源期刊
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
0.00%
发文量
20
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
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