Dowsing: a task-driven approach for multiple-view visualizations dynamic recommendation

Most users are able to obtain exploratory ideas from a data table but cannot clearly declare their analysis tasks as visual queries. Visualization recommendation methods can reduce the demand for data and design knowledge by extracting or referring information from existing high-quality views. However, most solutions cannot identify analysis tasks, which limits the accuracy of their recommendations. To address this limitation, we propose a deep learning and answer set programming-based approach to guide visualization recommendations by tracking potential analysis tasks and field preferences in exploration interactions. We demonstrate this approach via Dowsing, a mixed-initiative system for visual data exploration that automatically identifies and presents users’ potential analysis tasks and recommends visualizations during exploration. Additionally, Dowsing allows users to confirm and edit their intentions in multiple ways to adapt to changing analysis requirements. The effectiveness and usability of our approach are validated through quantitative experiments and two user studies.