Services for profiling, linking and exploring heterogenous datasets with applications areas in math, climatology, language research and lifelong learning.

DataGEMS is working on various services for profiling, linking and exploring heterogenous datasets with applications areas in math, climatology, language research and lifelong learning. They leverage state-of-the-art technology at the intersection of information systems, natural language processing, machine learning and agentic AI. Moreover, DataGEMS is developing a novel framework for storing and retrieving so-called analytics patterns called MOMA (Model Management Architecture). An analytical pattern is essentially a workflow of different operators ranging from SQL-queries to advanced machine learning algorithms for information extraction or complex data analytics. DataGEMS has developed a first end-to-end prototype covering the major services of the DataGEMS system to demonstrate the feasibility of our approach. The advantage of this approach is that it is generic and can be applied to any data-intensive domain that has heterogenous datasets (structured relational data, knowledge graphs, texts, images, etc.). However, for maximum effectiveness, the machine learning models need to be fine-tuned and adapted for highly-domain specific use cases by leveraging domain-specific ontologies and human-in-the-loop feedback from domain experts.