KIT 3DMM2O – Automated Data Clusters
Algorithm for structure and cluster detection in heterogeneous research data.
Overview
For the 3DMM2O Excellence Cluster, I developed an algorithm that analyzes, structures, and clusters research data without a prior schema. Results were presented at the E-Science Days Heidelberg. The system processes metadata from Zenodo repositories and identifies patterns across diverse data types.
Challenge
Research data from multiple groups comes in heterogeneous formats. Manual categorization is time-consuming and inconsistent.
Solution
Automated type detection and clustering algorithms process thousands of datasets, identifying patterns and groupings without predefined schemas.
Results & Impact
Over 10,000 datasets analyzed and clustered automatically. Research groups can now discover related work across the cluster efficiently.
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