Enhancing the Diamond Document Warehouse Model

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

International Journal of Data Warehousing and Mining Pub Date : 2020-10-01 DOI:10.4018/ijdwm.2020100101

M. Azabou, Ameen Banjar, J. Feki

引用次数: 0

Abstract

The data warehouse community has paid particular attention to the document warehouse (DocW) paradigm during the last two decades. However, some important issues related to the semantics are still pending and therefore need a deep research investigation. Indeed, the semantic exploitation of the DocW is not yet mature despite it representing a main concern for decision-makers. This paper aims to enhancing the multidimensional model called Diamond Document Warehouse Model with semantics aspects; in particular, it suggests semantic OLAP (on-line analytical processing) operators for querying the DocW.

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改进钻石文档仓库模型

在过去的二十年中，数据仓库社区特别关注文档仓库(DocW)范式。然而，一些与语义相关的重要问题仍然悬而未决，因此需要深入研究。实际上，DocW的语义开发还不成熟，尽管它代表了决策者的主要关注点。本文旨在从语义方面对多维模型——钻石文档仓库模型进行增强;特别是，它建议用于查询DocW的语义OLAP(在线分析处理)操作符。

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

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