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Business intelligence model for unstructured data management

机译:非结构化数据管理的商业智能模型

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Business Intelligence plays an important role in the organization for collecting, integrating, analyzing and transforming data to be useful for effective decision making process. Nowadays, organizations are flooded with various kinds of unstructured data such as e-mail, images, reports, maps, charts, publications. An effective and efficient business model of these data could help in decision making. Currently, there is no study done on the business intelligence model for managing unstructured data that can fulfil the organization needs. Therefore, the purpose of this paper is to improve the organization's business intelligence process through the exploitation of unstructured data that is owned by the organization. In this study, unstructured data are classified, enriched and complemented with diversity of data through the process of creating metadata for each unstructured data. Four main processes are proposed to transform unstructured data to structured data which are extraction, classification, storage and mapping of data classes. Each process and its activities are combined to produce an effective and efficient business intelligence model for unstructured data management. This model helps in generating new data and information that is more comprehensive and collective to help business intelligence through advanced analysis, decision-making process and planning new research areas. Output from this study is to make unstructured data as renewable assets that is easily accessible and used as a reference and foundation in business intelligence and decision making process.
机译:商业智能在本组织中发挥重要作用,用于收集,集成,分析和转换数据,以便有效决策过程。如今,组织被各种非结构化数据淹没,例如电子邮件,图像,报告,地图,图表,出版物。这些数据的有效和有效的商业模式可能有助于决策。目前,在商业智能模型上没有完成用于管理可以满足组织需求的非结构化数据的研究。因此,本文的目的是通过利用组织所拥有的非结构化数据来改善组织的商业智能流程。在本研究中,通过为每个非结构化数据创建元数据的过程来分类,丰富和补充,富集和互补数据。建议将非结构化数据转换为结构化数据的四个主要流程,该数据是数据类的提取,分类,存储和映射。组合每个过程及其活动,为非结构化数据管理生成有效和有效的商业智能模型。该模式有助于通过先进的分析,决策过程和计划新研究领域来帮助创造更全面和集体的新数据和信息,以帮助商业智能。本研究的产出是使非结构化数据作为可再生资产,可以易于访问,并用作商业智能和决策过程中的参考和基础。

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