SPP 1335: Scalable Visual Analytics: Interactive Visual Analysis Systems of Complex Information Spaces

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Data sets with rapidly growing complexity and dynamics are generated in research and development as well as in numerous application areas. A central challenge is to detect the important information and to communicate it to humans in an appropriate way. Interactive visual data analysis techniques extend the perceptual and cognitive abilities of humans with automatic data analysis techniques. Only by a combination of data analysis and visualisation techniques, an effective access to otherwise unmanageably complex data sets is possible. Visual analysis techniques make the unexpected more easily discoverable and help to gain new insights. The primary goal in the field of visual analytics is to represent real or abstract data graphically in a way that structural connections, relevant characteristics and other interesting properties of the data can be easily detected. Focus of the Priority Programme is research covering the theoretical foundations of new visual analytics algorithms, the development and practical implementation of scalable visual analytics techniques, as well as their integration and evaluation. A central challenge is scalability, which relates not only to the size of the data set but also to important properties of the data such as dimensionality, production rate, homogeneity, actuality, precision and completeness. In addition, the visual analytics techniques themselves should be scalable, which means that they should be interactive and convey the quality and relevance of the data. The scalability of visual analytics methods can only be achieved by a close cooperation between scientists from different areas of computer science. Participants in the Priority Programme come from the research areas visualisation, data analysis and data base technology, as well as human computer interaction, but also include other areas, which contribute to visual analytics, such as statistical analysis, geographical data analysis and cognitive science. Most of the projects combine at least two different research areas. The goal is to develop new techniques, which are successfully used in a concrete application and show significant improvements over existing approaches.