In many fields today data is produced continuously, potentially unbounded, and at high rates, which is termed as 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 management and analysis have been developed. In this course, you will get a deep understanding of these principles and techniques, such as query processing and optimization or data stream mining.
| Type | Lecture |
| Term | SS 2023 |
| Starting | 14.04.2023 at 8:30am |
| Mentor(s) |
Sandra Geisler |
| Assistant(s) |
Liam Tirpitz Anastasiia Belova |
| Moodle |
visit |
| First Weekly Day |
Friday at 8:30am Room: AH 6 |
| Second Weekly Day |
Monday at 12:30pm Room: AH I |
In this course, you will get a deep understanding of these principles and techniques, such as query processing and optimization or data stream mining.s, especially covering:
- 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
These principles will be reinforced by practically 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