OASW-Concept-Drift-Detection-and-Adaptation and MSANA-Online-Data-Stream-Analytics-And-Concept-Drift-Adaptation

These are ecosystem siblings—both are reference implementations of online learning frameworks from the same research lab (Western-OC2-Lab) that address concept drift in data streams, with OASW focusing on lightweight IoT scenarios while MSANA extends the approach to multi-stage network analytics.

Maintenance 0/25
Adoption 8/25
Maturity 16/25
Community 19/25
Maintenance 0/25
Adoption 7/25
Maturity 16/25
Community 17/25
Stars: 55
Forks: 19
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
Stars: 34
Forks: 8
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
Stale 6m No Package No Dependents
Stale 6m No Package No Dependents

About OASW-Concept-Drift-Detection-and-Adaptation

Western-OC2-Lab/OASW-Concept-Drift-Detection-and-Adaptation

An online learning method used to address concept drift and model drift. Code for the paper entitled "A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams" published in IEEE Internet of Things Magazine.

About MSANA-Online-Data-Stream-Analytics-And-Concept-Drift-Adaptation

Western-OC2-Lab/MSANA-Online-Data-Stream-Analytics-And-Concept-Drift-Adaptation

Data stream analytics: Implement online learning methods to address concept drift and model drift in dynamic data streams. Code for the paper entitled "A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems" published in IEEE Transactions on Industrial Informatics.

Scores updated daily from GitHub, PyPI, and npm data. How scores work