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.
@article{arxiv.1802.08621,
title = {DataSite: Proactive Visual Data Exploration with Computation of Insight-based Recommendations},
author = {Zhe Cui and Sriram Karthik Badam and Adil Yalçin and Niklas Elmqvist},
journal= {arXiv preprint arXiv:1802.08621},
year = {2018}
}
Comments
Databases, Information Visualization, Human Computer Interaction; Accepted at Information Visualization Journal