Inovace data miningového backendu pro systém EasyMiner

This thesis addresses the modernization of the data mining backend for the EasyMiner system by reimplementing its three core services – data, preprocessing, and mining – from Scala to Python. The original implementation suffered from significant technical debt, making it nearly impossible to build, maintain, or extend. The new backend is built on FastAPI for the web API layer, Celery for asynchronous task processing, and the pyARC library for association rule mining with CBA pruning support. Performance testing on nine datasets of varying size and complexity demonstrated that the Python implementation achieves comparable or better performance than the original, with the most significant improvement on large datasets – processing time for the KDD Cup 99 dataset was reduced from 49 minutes to 18 minutes. The system is fully containerized using Docker and orchestrated via Docker Compose and Kubernetes (Helm), enabling flexible deployment and horizontal scaling.