Propagating Visual Designs to Numerous Plots and Dashboards

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Saiful Khan, Phong Nguyen, Alfie Abdul-Rahman, Benjamin Bach, Min Chen, Euan Freeman, Cagatay Turkay

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 View presentation:2021-10-27T17:00:00ZGMT-0600Change your timezone on the schedule page
2021-10-27T17:00:00Z

Exemplar figure, described by caption below — Our novel propagation workflow makes it easy to propagate visual designs to numerous datasets. Reference visualizations are created for data streams. A search and activate process is used to propagate the reference visualization to other appropriate data streams. Ontology keywords are used to construct a query for suitable data stream combinations. Search results consist of ranked data stream combinations that match query parameters, although some results may not be suitable for propagation. A quality assurance carried out by an expert ensures the visual design is only propagated to suitable data, resulting in new visualizations that are deployed as web pages.

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Abstract

In the process of developing an infrastructure for providing visualization and visual analytics (VIS) tools to epidemiologists and modeling scientists, we encountered a technical challenge for applying a number of visual designs to numerous datasets rapidly and reliably with limited development resources. In this paper, we present a technical solution to address this challenge. Operationally, we separate the tasks of data management, visual designs, and plots and dashboard deployment in order to streamline the development workflow. Technically, we utilize: an ontology to bring datasets, visual designs, and deployable plots and dashboards under the same management framework; multi-criteria search and ranking algorithms for discovering potential datasets that match a visual design; and a purposely-design user interface for propagating each visual design to appropriate datasets (often in tens and hundreds) and quality-assuring the propagation before the deployment. This technical solution has been used in the development of the RAMPVIS infrastructure for supporting a consortium of epidemiologists and modeling scientists through visualization.