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DMiner: Dashboard Design Mining and Recommendation

Published 4 Sep 2022 in cs.HC and cs.GR | (2209.01599v2)

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, we identify design rules among those features and develop a recommender for dashboard design. We demonstrate the usefulness of DMiner through an expert study and a user study. The expert study shows that our extracted design rules are reasonable and conform to the design practice of experts. Moreover, a comparative user study shows that our recommender could help automate dashboard organization and reach human-level performance. In summary, our work offers a promising starting point for design mining visualizations to build recommenders.

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Citations (16)
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Summary

  • The paper presents DMiner, a framework that extracts design rules from Tableau dashboard features to automate optimal view arrangements.
  • It employs a RuleFit Binary Classifier to mine interpretable decision rules, integrating data, encoding, and layout interactions.
  • Evaluation through expert and user studies demonstrates DMiner's capability to deliver human-comparable, superior dashboard designs.

DMiner: Dashboard Design Mining and Recommendation

The paper "DMiner: Dashboard Design Mining and Recommendation" presents a novel framework, DMiner, aimed at improving dashboard design by mining design rules and automating the organization of multi-view dashboards. By leveraging a large dataset of dashboards sourced from Tableau workbooks, DMiner addresses two critical aspects of dashboard organization: arrangement and coordination. This essay will delineate the technical components of DMiner, evaluate its performance, and discuss its potential implications in dashboard visualization design.

Framework and Features

DMiner is structured to automatically extract design rules for multi-view dashboards and recommend optimal view arrangements and coordination. Its architecture consists of three key stages:

  1. Feature Extraction: The framework extracts features from dashboards, categorizing them into single-view features (data, encoding, layout) and pairwise-view features (data relationship, encoding relationship, layout, and coordination). Figure 1

    Figure 1: The workflow of DMiner. This paper proposes DMiner as a framework for dashboard design mining.

  2. Design Rule Mining: Utilizing a decision rule approach, DMiner models mappings between feature groups. The RuleFit Binary Classifier is employed to mine decision rules that are efficient and interpretable, focusing on the interaction between data and encoding features with layout arrangements and coordination.
  3. Recommendation System: The recommender employs mined rules to optimize dashboard designs. By generating candidate designs and scoring them based on rule obedience, the system provides recommendations with the highest scores. Figure 2

    Figure 2: The recommender framework that suggests the optimal dashboard designs.

Evaluation

DMiner's effectiveness was assessed through an expert study and a user study, showcasing its capability to align with expert design knowledge and significantly improve default dashboard designs.

  • Expert Study: Four dashboard design experts evaluated the extracted design rules. The study confirmed that the rules were reasonable, reflecting practical dashboard design insights, such as prioritizing prominent data views and maintaining logical proximity among related views.
  • User Study: Participants compared DMiner's results against default Tableau recommendations and designs by experienced human users. DMiner demonstrated superior performance, especially in dashboards with complex multi-view setups, by providing logical and helpful arrangements, combined with effective view coordination. Figure 3

    Figure 3: An example dashboard illustrating identified design rules.

Implications and Future Work

DMiner shows promise as a tool for enhancing dashboard visualization by reducing cognitive load and improving the logical flow of user interactions. Its ability to deliver human-comparable designs highlights its potential integration into existing visualization tools, aiding in both novice and expert user design workflows. However, areas for future exploration include integrating semantic data interpretations into design rule mining and expanding the framework to accommodate dashboards designed with various software beyond Tableau.

Conclusion

DMiner offers a data-driven approach to dashboard design, effectively bridging the gap between automated and expert-dashboard creation processes. By aligning mined design rules with established visualization practices, DMiner facilitates more intuitive, efficient, and user-friendly dashboard design. Future advancements in the framework may broaden its applicability to other visualization platforms and further refine its inference capabilities, contributing significantly to the field of data visualization.

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