Exploring Big Data Landscapes with Elastic Displays
Dietrich Kammer, Mandy Keck, Mathias Müller, Thomas Gründer, Rainer Groh · Gesellschaft für Informatik (GI) · 2017
In this paper, we propose a concept to help data analysts to quickly assess parameters and results of cluster algorithms. The presentation and interaction on a flexible display makes it possible to grasp the functioning of algorithms and focus on the data itself. Two interaction concepts are presented, which demonstrate the strength of elastic displays: a layer concept that allows the recognition of differences between various parameter settings of cluster algorithms, and a Zoomable User Interface, which encourages the in-depth analysis of clusters.