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Enhancing Business Intelligence Applications with Value-Driven Feedback and Recommendation

机译:增强具有价值驱动反馈和推荐的商业智能应用

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Business intelligence (BI) systems support activities such as data analysis, managerial decision making, and business-performance measurement. Our research investigates the integration of feedback and recommendation mechanisms (FRM) into BI solutions. We define FRM as textual, visual, and/or graphical cues that are embedded into front-end BI tools and guide the end-user to consider using certain data subsets and analysis forms. Our working hypothesis is that the integration of FRM will improve the usability of BI tools and increase the benefits that end-users and organizations can gain from data resources. Our first research stage focuses on FRM based on assessment of previous usage and the associated value gain. We describe the development of such FRM, and the design of an experiment that will test the usability and the benefits of their integration. Our experiment incorporates value-driven usage metadata - a novel methodology for tracking and communicating the usage of data, linked to a quantitative assessment of the value gained. We describe a high-level architecture for supporting the collection, storage, and presentation of this new metadata form, and a quantitative method for assessing it.
机译:商业智能(BI)系统支持数据分析,管理决策和业务性能测量等活动。我们的研究调查了反馈和推荐机制(FRM)的整合到BI解决方案。我们将FRM定义为嵌入到前端BI工具中的文本,视觉和/或图形提示,并指导最终用户考虑使用某些数据子集和分析表格。我们的工作假设是FRM的集成将提高BI工具的可用性,并提高最终用户和组织可以从数据资源获得的好处。我们的第一个研究阶段基于对之前使用的评估和相关价值增益的评估来侧重于FRM。我们描述了这种FRM的发展,以及实验的设计将测试其集成的可用性和益处。我们的实验包括价值驱动的使用元数据 - 一种用于跟踪和传达数据使用的新方法,与所获得的价值的定量评估相关联。我们描述了一种高级架构,用于支持本新元数据表单的收集,存储和呈现,以及用于评估它的定量方法。

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