Agile visual analytics in data science systems

Abstract

The practice of data science is ad hoc and agile, where needs and requirements evolve continuously and are resolved through collaboration among stakeholders. To support such practice visual analytics systems need to evolve as the needs and requirements in terms of data, users, tasks, medium, visualization, and interaction capabilities change continuously. In this paper we present a case study and illustrate several dimensions of the requirements in visual analytics. We put forward a vision of a dynamic agile visual analytics process and system model in support of data science, in which the user and system can cooperate to facilitate discovery while requirements change on demand. We argue that such a system needs an underlying language and algebra that defines not only operands and operators for performing visual analytics but also specifies guidelines that take them into account and produce useful visual analytics transformations leading to a specific insight. Our intent is not to present a fully developed system but rather a vision, illustrated through a use case. While the algebra presented here is sufficiently defined to illustrate our viewpoints, further refinement and completion is necessary to facilitate application in a visual analytics system.