DataSite: Proactive Visual Data Exploration with Computation of Insight-based Recommendations
This addresses the problem of inefficient data exploration for analysts by automating insight generation, though it is incremental as it builds on visualization recommendation systems.
The paper tackles the challenge of manual data exploration by introducing DataSite, a proactive visual analytics system that automatically computes and recommends insights, reducing the analyst's cognitive load and domain knowledge requirements. In a user study, DataSite showed significant improvement over existing systems, especially for complex analyses.
Effective data analysis ideally requires the analyst to have high expertise as well as high knowledge of the data. Even with such familiarity, manually pursuing all potential hypotheses and exploring all possible views is impractical. We present DataSite, a proactive visual analytics system where the burden of selecting and executing appropriate computations is shared by an automatic server-side computation engine. Salient features identified by these automatic background processes are surfaced as notifications in a feed timeline. DataSite effectively turns data analysis into a conversation between analyst and computer, thereby reducing the cognitive load and domain knowledge requirements. We validate the system with a user study comparing it to a recent visualization recommendation system, yielding significant improvement, particularly for complex analyses that existing analytics systems do not support well.