Propagating Visual Designs to Numerous Plots and Dashboards

TL;DR

This paper introduces a technical framework that enables rapid, reliable propagation of visual designs across numerous datasets and dashboards, streamlining visualization development for epidemiological research.

Contribution

It presents an integrated approach using ontologies, search algorithms, and a user interface to efficiently apply visual designs to multiple datasets and dashboards.

Findings

Successfully implemented in RAMPVIS infrastructure

Enhanced efficiency in visual design propagation

Improved quality assurance process

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.