In many fields today data is produced continuously, potentially unbounded, and at high rates, what is termed as a data stream. Applications in smart manufacturing, aerospace, particle physics, or stock exchange trading have a high demand to handle and analyze the massive data streams created. Due to their challenging characteristics specific technologies and methods for data streams management and analysis have been developed.
| Type | Lecture |
| Term | SS 2026 |
| Mentor(s) |
Sandra Geisler |
| Assistant(s) |
Soo-Yon Kim Anastasiia Belova |
In this course, you will get a deep understanding of principles and techniques for data stream management and analysis, such as query processing and optimization, as well as data stream mining. The course especially covers:
- Foundations of Data Streams
- Query processing and optimization for data streams
- Data Stream Processor systems and architectures
- Machine learning on data streams
- Metadata and data quality management for data streams
- Visualization of data streams
The theoretical part of the course will be supported by practical examples and exercises, using current systems and tools, such as Apache Storm or Apache Kafka.
- Basic / advanced database courses, e.g., Databases and Information Systems or Implementation of Databases
- Basic / advanced courses in Machine Learning, e.g., Data Science