We contribute a deep-learning-based method that assists in designing analytical dashboards for analyzing a data table. Given a data table, data workers usually need to experience a tedious and time-consuming process to select meaningful combinations of data columns for creating charts. This process is further complicated by the need of creating dashboards composed of multiple views that unveil different perspectives of data. Existing automated approaches for recommending multiple-view visualizations mainly build on manually crafted design rules, producing sub-optimal or irrelevant suggestions. To address this gap, we present a deep learning approach for selecting data columns and recommending multiple charts. More importantly, we integrate the deep learning models into a mixed-initiative system. Our model could make recommendations given optional user-input selections of data columns. The model, in turn, learns from provenance data of authoring logs in an offline manner. We compare our deep learning model with existing methods for visualization recommendation and conduct a user study to evaluate the usefulness of the system.
翻译:我们提供了一种深层次的学习方法,协助设计分析数据表的分析仪表板。根据数据表,数据工作者通常需要经历一个乏味和耗时的过程,以选择有意义的数据列组合来绘制图表。由于需要创建由多种观点组成的仪表板,以展示不同的数据视角,这一过程更加复杂。现有的建议多视可视化的自动化方法主要建立在手工设计的设计规则之上,产生亚优或不相干的建议。为了解决这一差距,我们提出了一种选择数据列和推荐多个图表的深层学习方法。更重要的是,我们把深学习模型纳入一个混合倡议系统。我们的模式可以针对数据列的可选用户输入选择作出建议。而模型反过来以离线方式从作者日志的原始数据中学习。我们将我们的深学习模型与现有的可视化建议方法进行比较,并进行用户研究,以评价系统的实用性。