Visual Data Analytics in e-Science Applications
Scientific and technical research methodology relies on effective analysis of increasingly large and complex data from simulations and experiments. To avoid bottlenecks in the scientific process it is thus important that the efforts spent on generating data is matched with development of data analysis technology and methodology.
The goal of this MCP is to develop visual analytics environments tailored to large-scale, complex, and dynamic data enabling interactive multi-scale analysis for knowledge discovery. A participatory design process involving domain and visualization experts is the core of the project ensuring relevance and practicability.
The MCP will be operated in a science-to-solution loop in which challenges are identified in the application areas and solutions provided in a cyclic workflow. This entails the development of novel visualization concepts as well as visual analytics platforms that support the research in the application areas in their day-to-day work. While the details of the research questions within the applications vary significantly there are fundamental concepts that are essential for all: interaction, filtering and explorations on multiple levels of detail to support scientific reasoning; scalability of the approaches in terms of complexity and data size; integrated solutions for dynamic data in a coherent way. These visualization challenges will be at the center of the MCP and driven by selected applications. The participatory development of data analytics solutions in the applications will lead to joint high impact publications in the visualization field as well as in the application domains.
People involved can be found here.