Centre for the Advanced Study of Collective Behaviour
Progressive visual analytics of collective behaviour data
This DFG-funded project (2017 - 2020) aims at bringing human and machine closer together in order to enhance VA for a more effective and efficient data analysis. A major part of this research proposal is to bridge the gap between human and machine learning (ML) in order to make complex model configuration and interaction more accessible and usable.
VASA (2011 - 2014): The goal of this BMBF-funded project coordinated by the University of Konstanz is to apply visual analytics to disaster prevention and crisis response, with a focus on critical infrastructures in logistics, digital networks, and power grids. This project will run from 2011 until 2014 and is realized by a consortium of research, industrial, and public partners from Germany.
The project in the context of the program “Research for Civil Security” is part of the High-Tech Strategy of the German Federal Government and is funded by the German Federal Ministry of Education and Research (BMBF).
In recent years the amount of protein sequence data has grown explosively. Much interesting information is hidden in the databases and possibly could answer many questions of scientific and medicinal interest. This project addresses the question what restrictions are protein sequences subjected to.
Using alignments of functionally equivalent proteins, regularities such as correlated positions or residue patterns can be searched for. These regularities are assumed to be necessary to ensure a specific fold and various cellular functions. The developed visual analytics tool VisAlign supports the analysis by combining an automatic calculation of correlations with an interactive visualization.
ViAMoD: Progress in positioning technologies has enabled the collection of huge amounts of data about movement of diverse types of objects in various domains, which has raised a demand for scalable methods for analyzing such data. In response, a number of methods and tools have appeared recently in data mining, geographic visualization, information visualization, and visual analytics. However, most of these approaches deal with movement data alone without taking into account the spatiotemporal context of the movement, which includes the properties of different places and different times and various spatial, temporal, and spatiotemporal objects affecting and/or being affected by the movement. This project aims at developing theoretical foundations and novel scalable methods for analyzing movement in context with the use of explicit context information available in the form of datasets as well as implicit context information available in the mind of human analyst.