Context:

Information visualization is paramount for the analysis of Big Data. The volume of data requiring interpretation is continuously growing. However, users are usually not experts in information visualization. Thus, defining the visualization that best suits a determined context is a very challenging task for them. Moreover, it is often the case that users do not have a clear idea of what objectives they are building the visualizations for. Consequently, it is possible that graphics are misinterpreted, making wrong decisions that lead to missed opportunities. One of the underlying problems in this process is the lack of methodologies and tools that non-expert users in visualizations can use to define their objectives and visualizations.

Objective:

The main objectives of this paper are to (i) enable non-expert users in data visualization to communicate their analytical needs with little effort, (ii) generate the visualizations that best fit their requirements, and (iii) evaluate the impact of our proposal with reference to a case study, describing an experiment with 97 non-expert users in data visualization.

Methods:

We propose a methodology that collects user requirements and semi-automatically creates suitable visualizations. Our proposal covers the whole process, from the definition of requirements to the implementation of visualizations. The methodology has been tested with several groups to measure its effectiveness and perceived usefulness.

Results:

The experiments increase our confidence about the utility of our methodology. It significantly improves over the case when users face the same problem manually. Specifically: (i) users are allowed to cover more analytical questions, (ii) the visualizations produced are more effective, and (iii) the overall satisfaction of the users is larger.

Conclusion:

By following our proposal, non-expert users will be able to more effectively express their analytical needs and obtain the set of visualizations that best suits their goals.

中文翻译：

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一种将用户需求自动转换为可视化的方法：实验验证

语境：

信息可视化对于分析大数据至关重要。需要解释的数据量在不断增长。但是，用户通常不是信息可视化方面的专家。因此，对于他们来说，定义最适合确定的上下文的可视化是一项非常具有挑战性的任务。此外，通常情况下，用户对于为其建立可视化对象的目标并不清楚。因此，可能会误解图形，做出错误的决定，从而导致错失良机。此过程中的潜在问题之一是缺少可视化领域的非专家用户可以用来定义其目标和可视化过程的方法和工具。

客观的：

本文的主要目的是（i）使数据可视化领域的非专家用户可以轻松地交流其分析需求；（ii）生成最适合其要求的可视化视图；以及（iii）评估我们建议的影响并参考一个案例研究，该案例描述了一项针对97位非专家用户进行数据可视化的实验。

方法：

我们提出了一种收集用户需求并半自动创建合适的可视化方法的方法。我们的建议涵盖了从定义需求到实现可视化的整个过程。该方法已通过多个小组的测试，以衡量其有效性和感知的实用性。

结果：

实验增加了我们对方法论实用性的信心。与用户手动面对相同问题的情况相比，它有了很大的改进。具体来说：（i）允许用户涵盖更多分析问题，（ii）产生的可视化效果更好，并且（iii）用户的总体满意度更高。

结论：

通过遵循我们的建议，非专家用户将能够更有效地表达他们的分析需求，并获得最适合其目标的可视化集。