Optimizing Visual Analytics Workflows: From Theory to Practice
For visual analytics researchers and practitioners, this work provides insights and a roadmap for bridging the gap between theory and practice in workflow optimization.
The paper investigates how to transform a theoretical methodology for optimizing visual analytics workflows into practical use, using action research across multiple domains. It identifies strengths, feasibility, and obstacles to broad deployment, and proposes a roadmap to address these challenges.
The principle of visual analytics (VA) is to provide integrated workflows where human-centric processes (e.g., visualization and interaction) and machine-centric processes (e.g., statistics and algorithms) complement each other. To implement this principle in practice, it is necessary to reason about the trade-offs among different processes and make optimal use of them in a workflow. Building on an existing ontology of the methodology for analyzing such trade-offs information-theoretically and for optimizing VA workflows systematically, we investigate ways to transform this methodology from theory to practice. In particular, we adopted the action research method. Through case studies in different application domains, VA researchers with different background knowledge and experiences offered their answers to several hypotheses about using the methodology in practice and proposed ways forward. In this paper, we present our collective analysis, the strengths and feasibility of this theory-based methodology, as well as the obstacles to its broad deployment in practice. To address these challenges, we outline a roadmap to remove such obstacles.