Medley: Intent-based Recommendations to Support Dashboard Composition

Aditeya Pandey, Arjun Srinivasan, Vidya Setlur

View presentation: 2022-10-20T21:33:00Z GMT-0600 Change your timezone on the schedule page
Exemplar figure, described by caption below
MEDLEY’s user interface. Users can select data attribute and intents from the input panel in the left. Collection recommendations are shown in the center of the screen. Views and widgets can be added from the recommendations to the dashboard canvas on the right.

Prerecorded Talk

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Fast forward

Despite the ever-growing popularity of dashboards across a wide range of domains, their authoring still remains a tedious and complex process. Current tools offer considerable support for creating individual visualizations but provide limited support for discovering groups of visualizations that can be collectively useful for composing analytic dashboards. To address this problem, we present MEDLEY, a mixed-initiative interface that assists in dashboard composition by recommending dashboard collections (i.e., a logically grouped set of views and filtering widgets) that map to specific analytical intents. Users can specify dashboard intents (namely, measure analysis, change analysis, category analysis, or distribution analysis) explicitly through an input panel in the interface or implicitly by selecting data attributes and views of interest. The system recommends collections based on these analytic intents, and views and widgets can be selected to compose a variety of dashboards. MEDLEY also provides a lightweight direct manipulation interface to configure interactions between views in a dashboard. Based on a study with 13 participants performing both targeted and open-ended tasks, we discuss how MEDLEY's recommendations guide dashboard composition and facilitate different user workflows. Observations from the study identify potential directions for future work, including combining manual view specification with dashboard recommendations and designing natural language interfaces for dashboard authoring.