Across three sessions, the submitted papers were presented on the topics “Visual Analytics Methods,” “Analyzing Movement and Events,” and “Applications of Visual Analytics.” In the subsequent panel discussion, “The Past, the Present and the Future of Visual Analytics,” Prof. Jörn Kohlhammer, Head of the “Information Visualization” department at Fraunhofer IGD, took part. Kohlhammer was involved in the founding of EuroVis and discussed the development of visual analytics with visualization experts Silvia Miksch (TU Vienna), Giuseppe Santucci (Sapienza University of Rome), and Samuel Kaski (Aalto University).

The submitted papers involving researchers from the Fraunhofer Institute for Computer Graphics Research IGD were:

On Quality Indicators for Progressive Visual Analytics

Authors: Angelini, Marco; May, Thorsten; Santucci, Giuseppe; Schulz, Hans-Jörg

A key component in using Progressive Visual Analytics (PVA) is to be able to gauge the quality of intermediate analysis outcomes. This is necessary in order to decide whether a current partial outcome is already good enough to cut a long-running computation short and to proceed. To aid in this process, we propose ten fundamental quality indicators that can be computed and displayed to gain a better understanding of the progress of the progression and of the stability and certainty of an intermediate outcome. We further highlight the use of these fundamental indicators to derive other quality indicators, and we show how to apply the indicators in two use cases.

Quantifying Uncertainty in Multivariate Time Series Pre-Processing

Authors: Bors, Christian; Bernard, Jürgen; Bögl, Markus; Gschwandtner, Theresia; Kohlhammer, Jörn; Miksch, Silvia

In multivariate time series analysis, pre-processing is integral for enabling analysis, but inevitably introduces uncertainty into the data. Enabling the assessment of the uncertainty and allowing uncertainty-aware analysis, the uncertainty needs to be quantified initially. We address this challenge by formalizing the quantification of uncertainty for multivariate time series preprocessing. To tackle the large design space, we elaborate key considerations for quantifying and aggregating uncertainty. We provide an example how the quantified uncertainty is used in a multivariate time series pre-processing application to assess the effectiveness of pre-processing steps and adjust the pipeline to minimize the introduction of uncertainty.