All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
- Initial release
- ML-powered UOM error detection (88-92% accuracy)
- Interactive visual dashboard with real-time updates
- KNIME workflow automation
- Sample dataset (10,000 records)
- Comprehensive documentation suite
- Community contribution guidelines
- Issue and PR templates
- 60+ engineered ML features for classification
- XGBoost classification algorithm
- Physics-based NIST validation rules
- Q-learning reinforcement learning agent (94% autonomy)
- Real-time dashboard visualization
- Batch processing (3,300 records/min)
- Support for CSV and Excel input formats
- Automated error correction with confidence scoring
- Root cause analysis and reporting
- Installation guide
- Dashboard user guide
- Architecture documentation
- Contributing guidelines
- Code of conduct
- Security policy
- API endpoint for system integration
- Multi-language support (Spanish, French, German)
- Mobile-responsive dashboard
- Advanced RL algorithms (Deep Q-Learning)
- Cloud deployment options (AWS, Azure, GCP)
- Real-time streaming data support
- Custom rule builder UI
- Performance monitoring dashboard
- Automated testing suite
- Docker containerization
- REST API wrapper
- Python SDK
- Jupyter notebook integration
- Automated model retraining
- Multi-tenant support
For more details, see the full release notes.