DMiner: Dashboard Design Mining and Recommendation
Yanna Lin, Haotian Li, Aoyu Wu, Yong Wang, Huamin Qu

TL;DR
This paper introduces DMiner, a data-driven system that mines design rules from dashboards and automates their organization, improving the efficiency and quality of dashboard creation through a large dataset, feature engineering, and a recommender system.
Contribution
The paper presents a novel approach for mining dashboard design rules and developing a recommender system that automates dashboard organization, validated by expert and user studies.
Findings
Design rules are consistent with expert practices.
The recommender system achieves human-level performance.
DMiner effectively automates dashboard layout and coordination.
Abstract
Dashboards, which comprise multiple views on a single display, help analyze and communicate multiple perspectives of data simultaneously. However, creating effective and elegant dashboards is challenging since it requires careful and logical arrangement and coordination of multiple visualizations. To solve the problem, we propose a data-driven approach for mining design rules from dashboards and automating dashboard organization. Specifically, we focus on two prominent aspects of the organization: arrangement, which describes the position, size, and layout of each view in the display space; and coordination, which indicates the interaction between pairwise views. We build a new dataset containing 854 dashboards crawled online, and develop feature engineering methods for describing the single views and view-wise relationships in terms of data, encoding, layout, and interactions. Further,…
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Taxonomy
TopicsData Visualization and Analytics
