Colvis—A structured annotation acquisition system for data visualization

Abstract

Annotations produced by analysts during the exploration of a data visualization are a precious source of knowledge. Harnessing this knowledge requires a thorough structure of annotations, but also a means to acquire them without harming user engagement. The main contribution of this article is a method, taking the form of an interface, that offers a comprehensive “subject-verb-complement” set of steps for analysts to take annotations, and seamlessly translate these annotations within a prior classification framework. Technical considerations are also an integral part of this study: through a concrete web implementation, we prove the feasibility of our method, but also highlight some of the unresolved challenges that remain to be addressed. After explaining all concepts related to our work, from a literature review to JSON Specifications, we follow by showing two use cases that illustrate how the interface can work in concrete situations. We conclude with a substantial discussion of the limitations, the current state of the method and the upcoming steps for this annotation interface.