Big Data Analytics
We are a group of computer scientists with ten years of experience developing data exploration and analysis methods in interdisciplinary research projects for
- detecting relationships in heterogeneous data sets,
- extracting relevant structures and patterns from large data sets,
- exploring n-D data spaces, and
- explaining the predictions/estimations of machine learning models to users.
Our research is motivated by real-world scientific applications such as remote sensing , fluid systems modeling, and Earth system modeling.
We adopt computer science methods from
- machine-learning/artificial intelligence,
- interactive visualization,
- scientific workflows, and
- component-based software development
to the specific requirements of real-world scientific applications.
Our research is grounded in computer science, but we develop our methods in close cooperation with users of the methods. To develop our methods, we engage in a "dialogue" with scientists by conducting a user and task analysis.
Find Relations among heterogeneous data
Geoscientists make use of data from sensors, geoarchives (such as core samples), and simulations to study the system Earth. The data describe Earth processes on various scales in space and time and by many different variables. Our methods facilitate the comparison of data from different sources and the integrated analysis of the heterogeneous data.
Research & Technologies:
Extract relevant information from large data sets
Current simulation models as well as observation systems generate large quantities of data. Our methods extract the important information from these data and present it in a compact and efficient manner to scientists.
Research & Technologies:
Exploration of multi-dimensional data
Geoscientists need to understand the behavior of Earth system processes based on multi-dimensional data. Our solutions enable scientists to explore spatio-temporal data to detect relevant information from multi-dimensional data.
Research & Technologies:
Explainable AI (XAI)
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Understanding machine learning methods such as deep neural networks has recently become a key topic as more and more users apply them to solve various problems and make critical decisions. Our methods extract meaningful information from the machine learning method and allow users to explore this information by using easy-to-understand visual encodings.
