Course taught in the ATHENS Program
Data streams are everywhere, from F1 racing over electricity networks to social media feeds. Data stream mining or Real-Time Analytics relies on and develops new incremental algorithms that process streams under strict resource limitations.
This course focuses on, as well as extends the methods implemented in open source tools as MOA and Apache SAMOA. Students will learn to how select and apply an appropriate method for a given data stream problem; they will learn how to design and implement such algorithms; and they will learn how to evaluate and compare different solutions.
Evaluation:
- 30% Lab Assignments
- 70% Final Test
Lecture Slides
- 1. Introduction Slides
- 2. Introduction to Data Science Slides
- 3. Stream Algorithmics Slides
- 4. Concept drift Slides
- 5. Evaluation Slides
- 6. Classification Slides
- 7. Ensemble Methods Slides
- 8. Clustering Slides
- 9. Distributed Streaming Slides
Session Labs
- 1. WEKA Lab Slides
- 2. Stream Algorithmic Lab Slides Example Dataset
- 3. MOA Lab I Slides MOOC Video
- 4. MOA Lab II Slides
- 5. Apache SAMOA Lab Slides