时间多维模型和OLAP操作符

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Waqas Ahmed, E. Zimányi, A. Vaisman, R. Wrembel
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引用次数: 3

摘要

通常,数据仓库(dw)中的数据使用多维模型的概念进行存储。通常,dw的内容和结构会由于几个原因而发生变化,例如,业务场景或技术的变化。为了做出准确的决策,DW模型必须允许存储和分析时变数据。本文解决了在数据仓库中跟踪数据历史的问题。为此,首先提出了传统MD模型的形式化,然后将其扩展为广义时间MD模型。该模型配备了一组具有时态语义的典型在线分析处理(OLAP)操作,这些操作被形式化为四种经典操作,即卷取、掷骰子、项目和钻取。最后,给出了从广义时间模型到关系模式的映射,以及在标准SQL中实现的时间OLAP操作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Temporal Multidimensional Model and OLAP Operators
Usually, data in data warehouses (DWs) are stored using the notion of the multidimensional (MD) model. Often, DWs change in content and structure due to several reasons, like, for instance, changes in a business scenario or technology. For accurate decision-making, a DW model must allow storing and analyzing time-varying data. This paper addresses the problem of keeping track of the history of the data in a DW. For this, first, a formalization of the traditional MD model is proposed and then extended as a generalized temporal MD model. The model comes equipped with a collection of typical online analytical processing (OLAP) operations with temporal semantics, which is formalized for the four classic operations, namely roll-up, dice, project, and drill-across. Finally, the mapping from the generalized temporal model into a relational schema is presented together with an implementation of the temporal OLAP operations in standard SQL.
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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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