论文标题
通过基于任务的建议进行视觉数据分析
Visual Data Analysis with Task-based Recommendations
论文作者
论文摘要
一般可视化建议系统通常会自动为数据集做出设计决策。但是，其中大多数只能修剪毫无意义的可视化，但没有推荐目标结果。本文贡献了TaskVis，这是一个面向任务的可视化建议系统，允许用户在接口上精确选择其任务。我们首先通过学术界和行业的调查总结了18个经典分析任务的任务基础。在此基础上，我们维持一个规则基础，该规则基础通过我们对分析任务的目标建模扩展了经验智慧。然后，我们基于规则的方法通过答案集编程列举了所有候选人的可视化。之后，生成的图表可以通过四个排名方案进行排名。此外，我们介绍了一种基于任务的组合建议策略，利用一组可视化来合作对数据集进行简要了解。最后，我们通过一系列用例和用户研究来评估任务案例。
General visualization recommendation systems typically make design decisions for the dataset automatically. However, most of them can only prune meaningless visualizations but fail to recommend targeted results. This paper contributes TaskVis, a task-oriented visualization recommendation system that allows users to select their tasks precisely on the interface. We first summarize a task base with 18 classical analytic tasks by a survey both in academia and industry. On this basis, we maintain a rule base, which extends empirical wisdom with our targeted modeling of the analytic tasks. Then, our rule-based approach enumerates all the candidate visualizations through answer set programming. After that, the generated charts can be ranked by four ranking schemes. Furthermore, we introduce a task-based combination recommendation strategy, leveraging a set of visualizations to give a brief view of the dataset collaboratively. Finally, we evaluate TaskVis through a series of use cases and a user study.
