Decision makers spend significant time and effort interpreting information derived from large multidimensional databases; data are usually represented by several dashboard diagrams. The Complexity Design (CoDe) methodology provides a technique modeling graphical reports on data extracted by a data warehouse, where the charts composing the dashboard diagrams are integrated with a visual representation of the logical relationships among them. The generated visualizations (CoDe Graphs) are automatically obtained by connecting dashboard diagrams through graphical conceptual links. After analyzing the state of the art regarding the evaluation of graphical representation comprehensibility, we propose a classification of those evaluation approaches and evaluate the comprehensibility of CoDe Graphs concerning dashboard reports through a controlled experiment, involving 47 participants. Results show that CoDe Graphs reduce participants effort while improving effectiveness and efficiency in comprehension tasks.

中文翻译：

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复杂性设计图表报告的用户理解

决策者花费大量时间和精力来解释从大型多维数据库中获取的信息；数据通常由几个仪表板图表示。复杂性设计 (CoDe) 方法提供了一种对数据仓库提取的数据进行图形报告建模的技术，其中构成仪表板图的图表与它们之间逻辑关系的可视化表示相集成。生成的可视化（代码图）是通过图形概念链接连接仪表板图自动获得的。在分析了有关图形表示可理解性评估的最新技术之后，我们提出了这些评估方法的分类，并通过一项涉及 47 名参与者的受控实验评估了有关仪表板报告的代码图的可理解性。结果表明，代码图减少了参与者的努力，同时提高了理解任务的有效性和效率。