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A Metadata-based Approach to Leveraging the Information Supply of Business Intelligence Systems

机译:基于元数据的利用商业智能系统信息供应的方法

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Ensuring adequate information provision continues to be a key challenge of corporate decision making and the usage of business intelligence systems. As a matter of fact, the situation becomes increasingly paradox: Whereas decision makers struggle to specify their information requirements and spend much time on obtaining the information they believe to require, the amount of information supplied by business intelligence systems grows at a speed that makes it hard to keep track. Thus, it is very likely that the required information or suitable alternatives are available, but neither found nor used. Instead, manual searching causes considerable opportunity cost. Existing approaches to information requirements analysis pay attention to incorporate information supply, but do not provide means for leveraging it in a systematic and IT supported manner. As a first step to close this research gap, we propose a metadata-based approach consisting of a procedure model and formalism that help identify a suitable subset of the information supplied by an existing business intelligence system. The formalism is specified using set theory and first-order logic to provide a general foundation that may be integrated into different conceptual modelling approaches.
机译:确保提供足够的信息仍然是公司决策和使用商业智能系统的主要挑战。实际上,这种情况变得越来越自相矛盾:尽管决策者努力地规定他们的信息要求并花费大量时间来获取他们认为需要的信息,但是商业智能系统提供的信息量以使其迅速增长的速度增长。很难跟踪。因此,很有可能获得了所需的信息或合适的替代方法,但是找不到或未使用过。相反,手动搜索会导致可观的机会成本。现有的信息需求分析方法关注合并信息提供,但未提供以系统化和IT支持的方式利用信息的手段。作为弥合这一研究空白的第一步,我们提出了一种基于元数据的方法,该方法由过程模型和形式主义组成,可帮助识别现有商业智能系统提供的信息的合适子集。使用集合论和一阶逻辑来指定形式主义,以提供可以集成到不同概念建模方法中的一般基础。

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