Toward a Taxonomy for Adaptive Data Visualization in Analytics Applications.

ABSTRACT: Data analytics as a field is currently at a crucial point in its development, as a commoditization takes place in the context of increasing amounts of data, more user diversity, and automated analysis solutions, the latter potentially eliminating the need for expert analysts. A central hypothesis of the present paper is that data visualizations should be adapted to both the user and the context. This idea was initially addressed in Study 1, which demonstrated substantial interindividual variability among a group of experts when freely choosing an option to visualize data sets. To lay the theoretical groundwork for a systematic, taxonomic approach, a user model combining user traits, states, strategies, and actions was proposed and further evaluated empirically in Studies 2 and 3. The resul

SUBMITTER: Poetzsch T 

PROVIDER: S-EPMC7861272 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature