Information visualization plays a key role in business intelligence analytics. With ever larger amounts of data that need to be interpreted, using the right visualizations is crucial in order to understand the underlying patterns and results obtained by analysis algorithms. Despite its importance, defining the right visualization is still a challenging task. Business users are rarely experts in information visualization, and they may not exactly know the most adequate visualization tools or patterns for their goals. Consequently, misinterpreted graphs and wrong results can be obtained, leading to missed opportunities and significant losses for companies. The main problem underneath is a lack of tools and methodologies that allow non-expert users to define their visualization and data analysis goals in business terms. In order to tackle this problem, we present an iterative goal-oriented approach based on the i* language for the automatic derivation of data visualizations. Our approach links non-expert user requirements to the data to be analyzed, choosing the most suited visualization techniques in a semi-automatic way. The great advantage of our proposal is that we provide non-expert users with the best suited visualizations according to their information needs and their data with little effort and without requiring expertise in information visualization.

翻译：信息可视化在商业智能分析中发挥着关键作用。随着需解读的数据量日益庞大，选择恰当的可视化方式对于理解分析算法所揭示的潜在模式和结果至关重要。然而，尽管其重要性不言而喻，确定合适的可视化方案仍是一项具有挑战性的任务。商业用户通常并非信息可视化领域的专家，他们可能并不清楚哪种可视化工具或模式最契合自身目标。由此可能导致图形被曲解、获得错误结果，进而使企业错失良机并蒙受重大损失。其根本问题在于缺乏能够让非专业用户从商业角度定义其可视化及数据分析目标的工具与方法。为解决此问题，我们提出了一种基于i*语言的迭代式目标导向方法，用于自动推导数据可视化方案。该方法将非专业用户的需求与待分析数据相关联，以半自动方式选择最合适的可视化技术。我们提出的方案最大优势在于，能够根据用户的信息需求和数据，无需耗费过多精力且无需具备信息可视化专业知识，即可为非专业用户提供最合适的可视化方案。