Visual-Interactive Data Exploration
In Visual Analytics we research effective systems for exploration in large and complex data sets. Our aim is to combine scalable visual data representations with appropriate automatic data analysis methods. A tight integration of Visualization and Data Analysis in interactive systems can help to find patterns and details of interest in large data. Our work falls into the following areas: Surveys and Foundations of Visual Analytics; Visual Analysis of High-Dimensional and Relational Data; Visual Analysis of Spatio-Temporal Data; Visual Search in Research Data.
- Surveys and Foundations of Visual Analytics
- Visual Analysis of High-Dimensional and Relational Data
- Visual Analysis of Spatial and Temporal Data
- VAST Challenge Participation
Contact: Prof. Dr. Tobias Schreck
Search Interfaces and Semantic Annotation
Effective user access to large visual and abstarct data content can be provided by appropriate visual seaerch interfaces which allow to formulate queries in an intuitive way. Also, showing search results in context of clusters and patterns in large data is helpful for retrieval. We develop visual interfaces for seraching an annotating in e.g., time series data, scatter plot data, trajectory data, and also, 3D object data.
Contact: Prof. Dr. Tobias Schreck
Submission and Review System
In this research area we consider novel methods for indexing, annotating, serving, and preserving generalized documents. This includes methods for content-based search in 3D objects, the development of web architectures for distributed search and delivery of content, and the definition of visual-interactive search user interfaces for content access. Among others, important application domains include digital architecture (e.g., analysis and archival of 3D building data) and digital archaeology (e.g., markup, indexation and restoration of 3D artifact data).
Contact: Prof. Dr. Tobias Schreck
Photo credits: © T. Schreck, T. Tekusova, J. Kohlhammer, and D. Fellner. Trajectory-based visual analysis of large nancial time series data. ACM SIGKDD Explorations, Special Issue on Visual Analytics, 9:30-37, 2007. // L. Zhang, A. Stoel, M. Behrisch, S. MittelstŁadt, T. Schreck, R. Pompl, S. Weber, H. Last, and D. Keim. Visual analytics for the big data era - a comparative review of state-of-the-art commercial systems. In Proc. IEEE Symposium on Visual Analytics Science and Technology, pages 173-182, 2012.