Our MissionThe iVis Group at Linköping University focuses on the explorative analysis and visualization of typically large and complex information spaces, for example in environmental research, transportation systems, social sciences, or artificial intelligence. Our vision is to attack the big data challenge by a combination of human-centered data analysis and interactive visualization for deriving meaning from the data and final decision making. Our research is highly relevant for academia and economy as both science and industry make increasing use of data-intensive technologies.
We take a human-centered and problem-oriented visualization approach: human-centered visualization deals with the development of interactive visualization techniques in consideration of user- and task-related information to explore and analyze complex data sets efficiently. This course of action combines aspects of different research fields, such as information and scientific visualization, human-computer interaction, information design, cognition, but also the particular application field. From all areas of visualization, we mainly focus on information visualization (abbr. InfoVis) which centers on the visualization of abstract data, e.g., hierarchical, networked, or tabular information sources. While the development of human-centered information visualization approaches and systems, user abilities and requirements, visualization tasks, tool functions, interactive features, and suitable visual representations are equally taken into account.
In contrast to visualization, data mining or machine learning are traditionally computer-centered. But to address the big data challenge more efficiently and to increase the trust into the analytical results, we have to use the advantages of both approaches synergistically, which is the main aspect of visual analytics. The design and implementation of visual analytics tools is one of the most promising approaches to cope with the ever increasing amount of data produced every day and allows new insights and beneficial discoveries.
The purpose of research in these areas is to develop novel methods and tools that are able to efficiently support analysts from various data domains. Our new visualization techniques enable them to solve difficult analytical problems (referring to the famous V’s of Big Data) and to identify and extract meaningful information from the data while improving the speed, accuracy, and completeness of their understanding. We are engaged in a wide range of research aspects which encompass the development of new algorithmic approaches for the extraction of patterns and relationships in data, the visual and auditory representation of these features, the use of machine learning approaches in visualization and vice versa, as well as the study of perceptual mechanisms and novel evaluation methodologies.