Meta‐Storyboard Using Feature‐Action Patterns: Concept, Techniques, and Case Studies

Abstract

This paper introduces the concepts of meta‐storyboard (MSB) and meta‐storyboard authoring for data‐driven storytelling. Traditionally, in data‐driven storytelling, a storyboard is predefined for a given data set and the information an author wants to convey. However, such a static process does not account for dynamic data and does not scale up to the need to produce visual stories for different individual viewers and contexts. In a meta‐storyboard, an author anticipates potential features that may appear in dynamically arriving or selected data and consequently defines visualization actions to convey explanations about these features. An MSB thus includes feature‐action patterns as reusable design patterns for anticipated data features, viewers' requests, and application contexts. Through two case studies, we first developed the concept of MSB as a standalone software for communicating time series data in the context of COVID‐19; then, we broadened the concept as an API to tell stories about machine learning workflows. MSB is complementary to traditional methods for authoring storytelling visualizations, while providing an efficient means to construct data‐dependent storyboards for different data streams of similar contexts.