Our Mission
The ISOVIS group at Linnaeus University mainly focuses on the explorative analysis and visualization of typically large and complex information spaces, for example in biochemistry, humanities, or software engineering. Our vision is to attack the big data challenge by a combination of human-centered data analysis and interactive visualization for decision making. These research topics are highly relevant for academia and economy as both science and industry make increasing use of data-intensive technologies.
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. Sensor data measured during the usage of a visualization (e.g. from brain-computer interfaces, eye trackers, etc.) may also be involved. This approach combines aspects of different research areas, such as information and scientific visualization, human-computer interaction, information design, cognition, but also the particular application field.
From all subfields of visualization, we mainly focus on information visualization which centers on the visualization of abstract data, e.g., hierarchical, networked, or symbolic information sources. While the development of human-centered information visualizations, 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 more computer-centered. But to address the big data challenge, we have to use the advantages of both approaches synergistically, which is the main feature of visual analytics. Then, the analyst can focus his/her perceptual and cognitive capabilities on the analytical processes while using advanced computational methods to support and enhance the discovery process. 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.
