Reliable Visual Analytics, a Prerequisite for Outcome Assessment of Engineering Systems

Abstract

Various evaluation approaches exist for multi-purpose visual analytics (VA) frameworks. They are based on empirical studies in information visualization or on community activities, for example, VA Science and Technology Challenge (2006-2014) created as a community evaluation resource to "decide upon the right metrics to use, and the appropriate implementation of those metrics including datasets and evaluators" 1 . In this paper, we propose to use evaluated VA environments for computer-based processes or systems with the main goal of aligning user plans, system models and software results. For this purpose, trust in VA outcome should be established, which can be done by following the (meta-)design principles of a human-centered verification and validation assessment and also in dependence on users' task models and interaction styles, since the possibility to work with the visualization interactively is an integral part of VA. To define reliable VA, we point out various dimensions of reliability along with their quality criteria, requirements, attributes and metrics. Several software packages are used to illustrate the concepts.

GUI-Based Data Visualisation Tool for Option C IPMVP using Jupyter Notebook

“…Software sequence verification activities allow reducing time for development since all unpredictable things can be detected in advance. A thorough process in verification flow helped in developing effective validation flow [14] especially to a visual analytic which involves a lot of data to be processed [15].…”